<reasoning_content>
Thinking Process:

1.  **Analyze the Request:**
    *   Goal: Implement a basic Job Queue in Python.
    *   Class name: `JobQueue`.
    *   Methods:
        *   `add_job(job_id: str, data: dict) -> str`: Adds job, returns `job_id`.
        *   `get_result(job_id: str) -> dict | None`: Retrieves result of a completed job.
    *   Constraints: FIFO order, internal storage, unique job IDs.
    *   Output format: Python code only, no explanations.

2.  **Determine Data Structures:**
    *   To support FIFO, a `collections.deque` or a standard list (acting as a queue) is appropriate. `deque` is generally more efficient for popping from the left, but a simple list works for a "basic" implementation. Let's use `collections.deque` for best practices or a list for simplicity. Given it's a "basic" queue, a list is fine, but `deque` is safer for FIFO semantics.
    *   To store results, a dictionary mapping `job_id` to `result` is needed.
    *   To track pending jobs, we need to store the job data in the queue.

3.  **Drafting the Class Structure:**

   ```python
   from collections import deque
   from typing import Optional, Dict, Any

   class JobQueue:
       def __init__(self):
           self._queue = deque() # Stores job_ids or job objects
           self._results = {}    # Stores job_id -> result
           # Maybe store job data? The prompt implies data is passed in,
           # but doesn't explicitly say we need to store the input data
           # for retrieval later, just that we process it.
           # However, usually a queue holds the job definition.
           # Let's store the job_id in the queue.
           # Actually, let's store the job_id. The data is processed "somewhere".
           # Since this is a basic implementation, I need to simulate "processing"
           # or just assume the result is populated later?
           # The prompt says "Jobs should be processed... until they are processed".
           # But it doesn't provide a `process()` method or a worker thread.
           # It just asks for `add_job` and `get_result`.
           # This implies the `JobQueue` might just be a storage mechanism,
           # or I need to implement a dummy processing mechanism.
           # Looking at the example:
           # queue.add_job(...)
           # # After processing... (Comment implies external processing or time passing)
           # result = queue.get_result(...)
           #
           # If I just store the job_id in a queue, how does it get processed?
           # A real JobQueue usually has a worker loop.
           # However, the requirements are minimal.
           # 1. add_job
           # 2. get_result
           # 3. FIFO processing.
           #
           # If I don't implement a worker, "processed" is ambiguous.
           # But usually, "Job Queue" implies a system that takes a job and produces a result.
           # Since I cannot assume an external worker exists in this snippet,
           # I should probably implement a simple synchronous processing or just
           # store the result if the prompt implies a mock environment.
           #
           # Wait, looking at requirement 3: "The queue should store jobs internally until they are processed".
           # This suggests the queue holds the pending jobs.
           # Requirement 2: "Jobs should be processed in FIFO order".
           # This implies an execution mechanism.
           #
           # Let's implement a simple synchronous execution for the sake of a functional class,
           # or just a structure that allows a worker to pull from it.
           # BUT, the example shows `add_job` then `get_result`.
           # If I process synchronously inside `add_job`, it's not really a queue, it's a direct call.
           # If I process asynchronously, I need threading.
           #
           # Let's look at the constraints again.
           # "Write only the Python code".
           #
           # Maybe the "processing" is simulated or the class is just the container?
           # If it's just a container, `get_result` would return None unless a result was set.
           # But the prompt asks for "Jobs should be processed".
           #
           # Let's assume a standard pattern:
           # 1. Queue holds pending jobs.
           # 2. A method (maybe implicit or just logic) moves job from pending to completed.
           #
           # Since no `process()` method is requested, maybe the jobs are processed immediately?
           # Or maybe the `JobQueue` is expected to be used with a worker?
           #
           # Let's stick to the most literal interpretation of a Queue data structure that tracks state.
           # However, to satisfy "Jobs should be processed in FIFO order", there must be logic moving them.
           #
           # Let's implement a simple worker thread or just synchronous processing for simplicity?
           # Synchronous processing defeats the purpose of a queue usually, but fits the "basic" description without external dependencies.
           # OR, perhaps the "processing" is just moving it from a `pending` list to a `completed` dict?
           #
           # Let's look at the example again.
           # job_id = queue.add_job(...)
           # # After processing...
           # result = queue.get_result(...)
           #
           # This comment suggests time passes or an action happens.
           # If I write code that just stores the job, `get_result` returns None.
           # If I write code that processes it immediately, `get_result` returns the result.
           #
           # Let's assume the user wants a class that *manages* the queue, and perhaps a simple simulation of processing or just the storage logic.
           # However, "Jobs should be processed" is a functional requirement.
           #
           # Let's implement a basic synchronous processing for the sake of the example working,
           # or perhaps a `process_next` method? No, not requested.
           #
           # Alternative interpretation: The class is just a wrapper. The "processing" is external.
           # But requirement 2 says "Jobs should be processed in FIFO order".
           # This is a constraint on the class behavior.
           #
           # Let's implement a simple synchronous execution of the job data for demonstration,
           # or just store the result as the data itself (mock processing).
           #
           # Actually, a common interview question pattern for "Basic Job Queue" without threading requirements often implies:
           # 1. Store job.
           # 2. Have a way to retrieve the job (FIFO).
           # 3. Store result.
           #
           # But `get_result` is specific.
           #
           # Let's try to implement a simple synchronous execution that returns a mock result,
           # or simply stores the input data as the result (assuming "processing" is a black box).
           #
           # Let's go with a design where `add_job` puts it in a deque.
           # To satisfy "processed in FIFO", we need a consumer.
           # Since no consumer is defined, maybe `add_job` triggers processing?
           # If `add_job` triggers processing synchronously, it's FIFO (LIFO stack behavior effectively for the caller, but the queue processes one by one).
           #
           # Let's consider the possibility that the user just wants the data structure to hold jobs and results, and the "processing" logic is up to the user, but the *structure* must support FIFO retrieval if there were a worker.
           # BUT, "Jobs should be processed" sounds like a requirement for the class to do it.
           #
           # Let's implement a simple background thread? No, that's complex for "basic".
           # Let's implement synchronous processing.
           #
           # Wait, if I process synchronously in `add_job`, the queue is empty immediately.
           #
           # Let's look at the requirements again carefully.
           # 1. `add_job`
           # 2. `get_result`
           # 2. FIFO order.
           # 3. Store jobs internally until processed.
           #
           # This implies a state transition: Pending -> Processed.
           #
           # Let's implement a method `process_jobs`? No, not in requirements.
           #
           # Maybe the "processing" is just moving the item from the queue to the results dict?
           # But who triggers that?
           #
           # Let's assume the prompt implies a synchronous execution for simplicity, or perhaps the `JobQueue` is meant to be used like:
           # q.add_job(...)
           # q.add_job(...)
           # q.process() # Not requested
           #
           # Okay, let's look at the example usage again.
           # `queue.add_job`
           # `# After processing...`
           # `queue.get_result`
           #
           # This strongly implies that the processing happens *between* the add and the get, potentially externally or implicitly.
           # If I provide a class where `add_job` just appends to a deque, and `get_result` looks in a results dict, `get_result` will return None unless something puts data in the results dict.
           #
           # Maybe I should implement a simple worker loop in `__init__`?
           # Or maybe the "job" is just the data, and "processing" is a mock function?
           #
           # Let's create a simple implementation that simulates processing.
           # If `add_job` is called, it adds to queue.
           # If we want to satisfy "Jobs should be processed", maybe we process them on demand?
           # Or maybe we process them immediately?
           #
           # Let's try a different angle. Maybe the class is just a container, and the "FIFO" requirement refers to how jobs are *retrieved* for processing if there was a `get_job` method? But there isn't.
           #
           # Let's assume the standard "Task Queue" pattern:
           # 1. Queue holds pending tasks.
           # 2. Results are stored.
           # 3. A worker processes them.
           #
           # Since I must write *only* the code and satisfy the requirements, and the requirements are slightly ambiguous regarding the *trigger* for processing, I will implement a simple synchronous processing for the sake of the example working, or a simple mock.
           #
           # Actually, looking at "The queue should store jobs internally until they are processed", this implies the job stays in the queue.
           # If I process it synchronously in `add_job`, it doesn't stay in the queue.
           #
           # Maybe the intended solution is a simple structure where `add_job` adds to a deque, and `get_result` checks a dict. The "processing" is an external concept, but to make the example work, I might need to provide a way to process.
           #
           # However, strict adherence to requirements:
           # 1. Class `JobQueue`.
           # 2. Methods `add_job`, `get_result`.
           # 3. FIFO processing (implies order of completion or order of execution).
           # 4. Store internally.
           #
           # Let's implement a simple synchronous execution that returns a result, but stores the job in the queue first to satisfy "store internally"? No, that's contradictory.
           #
           # Let's assume the prompt is a bit loose and wants a data structure that *can* be processed in FIFO.
           # But "Jobs should be processed" is a constraint.
           #
           # Let's implement a simple background thread. It's the most robust way to satisfy "processed in FIFO" while allowing `add_job` to return immediately and `get_result` to wait or check.
           # But threading adds complexity.
           #
           # Let's try a simpler approach:
           # Maybe the "processing" is just a placeholder.
           # Let's assume the user wants to see the queue logic.
           #
           # Let's implement a `JobQueue` that uses a `deque` for pending jobs and a `dict` for results.
           # To satisfy "processed in FIFO", I will add a simple method or logic?
           # Wait, the requirements list specific methods. It does *not* list a `process` method.
           # This implies the processing might happen automatically or the user is expected to know how to trigger it, OR the processing is synchronous.
           #
           # If processing is synchronous in `add_job`:
           # 1. Add to queue (store internally).
           # 2. Pop from queue (FIFO).
           # 3. Process.
           # 4. Store result.
           # 5. Return job_id.
           # This satisfies all constraints technically, though it's not an async queue.
           #
           # Let's refine this:
           # `add_job`:
           #   self.queue.append(job_id)
           #   # Simulate processing?
           #   # If I process here, it's synchronous.
           #
           # Let's look at the example usage comment: `# After processing...`
           # This implies a gap.
           #
           # If I write a class that just stores the job, the example fails (result is None).
           # If I write a class that processes immediately, the comment is weird but the code works.
           #
           # Let's consider that maybe the "processing" is just moving data from input to output.
           #
           # Let's try to implement a simple synchronous processing that transforms the data slightly, just to show it "processed".
           # Or, perhaps simpler: The prompt is a test of data structures.
           #
           # Let's write a class that:
           # 1. Stores jobs in a deque.
           # 2. Has a `process` loop? No, not requested.
           #
           # What if I interpret "Jobs should be processed in FIFO order" as a constraint on how `get_result` or internal logic works?
           # No, processing usually means execution.
           #
           # Let's assume the prompt implies a synchronous execution for simplicity, as it's a "basic" queue.
           #
           # Wait, if I process synchronously, why store it in a queue first?
           # Maybe to log it?
           #
           # Let's look at the requirements one more time.
           # "The queue should store jobs internally until they are processed"
           # This implies the state "in queue" exists before "processed".
           #
           # If I do:
           # ```python
           # def add_job(self, job_id, data):
           #     self.queue.append({'id': job_id, 'data': data})
           #     # Process it?
           #     job = self.queue.popleft() # FIFO
           #     result = self._process(job)
           #     self.results[job_id] = result
           #     return job_id
           # ```
           # This satisfies:
           # - Add job.
           # - Store internally (briefly).
           # - Process in FIFO (popleft).
           # - Return job_id.
           # - `get_result` returns the result.
           #
           # This seems like the most logical interpretation for a single-threaded, basic implementation without external workers.
           #
           # However, usually "Queue" implies decoupling.
           # But without a `process` method or threading, synchronous processing is the only way to generate a result.
           #
           # Let's consider if `get_result` should block? No, returns `dict | None`.
           #
           # Let's implement the synchronous processing approach. It's safe and meets all text requirements.
           #
           # Wait, if I process it immediately, the queue is empty. "Store jobs internally until they are processed".
           # If I process it immediately, it is stored internally *while* being processed (in the variable `job`), but maybe not in the queue structure `self.queue` for long.
           #
           # Let's try to keep it in the queue until processed.
           # But who processes it?
           #
           # Maybe the class is just a container, and the "processing" is a responsibility of the user, but the class must support the FIFO retrieval?
           # But `get_result` gets the *result*, not the job.
           #
           # Okay, let's step back.
           # If this is a coding test question, usually they expect:
           # 1. `collections.deque` for the queue.
           # 2. A dictionary for results.
           # 3. Maybe a simple worker thread?
           #
           # Let's check if there's a simpler interpretation.
           # Maybe "processed" just means "completed" and the user is expected to call a method not listed? No, "Requirements: 1. Create a JobQueue class with the following methods...". It lists specific methods.
           #
           # If I only implement `add_job` and `get_result`, and no processing logic, `get_result` will always return None (unless I hardcode a result).
           # That seems useless.
           #
           # Therefore, `add_job` or the class initialization must handle processing.
           #
           # Let's implement a simple synchronous execution.
           # To make it look like a queue, I'll append to deque, then pop and process.
           #
           # Wait, if I have multiple jobs, `add_job` processes one.
           # If I call `add_job` twice:
           # 1. Add J1. Process J1. Result J1.
           # 2. Add J2. Process J2. Result J2.
           # Order is preserved.
           #
           # What if I want to batch process?
           # The prompt doesn't specify.
           #
           # Let's try a different approach. Maybe the "processing" is just a mock, and I should just store the data as the result?
           # `result = data`?
           #
           # Let's write the code for a basic queue that stores jobs.
           # I will add a simple `_process` method that just returns the data (or a modified version) to simulate work.
           # And I will call it synchronously in `add_job` to ensure the example works.
           #
           # Actually, looking at the code snippet provided in the prompt:
           # ```python
           # queue = JobQueue()
           # job_id = queue.add_job("job1", {"task": "process_data"})
           # # After processing...
           # result = queue.get_result("job1")
           # ```
           # The comment `# After processing...` suggests that processing is not instantaneous or happens in the background.
           # If I process synchronously, the comment is redundant but acceptable.
           #
           # However, a better implementation for a "Job Queue" usually involves a worker.
           # But writing a thread-safe queue with a worker might be overkill for "basic".
           #
           # Let's look for a middle ground.
           # Maybe the `JobQueue` is just the storage, and the prompt assumes an external processor?
           # But "Jobs should be processed in FIFO order" is a requirement for the *Queue*.
           #
           # Let's assume the prompt wants a class that *manages* the queue and processes jobs.
           # I will implement a simple background thread for processing. It's the most correct interpretation of "Queue" (producer/consumer).
           #
           # Requirements check:
           # 1. `add_job`: puts job in deque.
           # 2. `get_result`: checks results dict.
           # 3. FIFO: Worker pops from left of deque.
           # 4. Store internally: Deque holds it.
           #
           # Implementation details:
           # - `threading.Thread` for worker.
           # - `queue` module or `collections.deque` + `threading.Lock`/`Condition`.
           # - `queue.Queue` is thread-safe and FIFO. That's perfect.
           #
           # Let's use `queue.Queue`.
           #
           # Code plan:
           # ```python
           # import queue
           # import threading
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._job_queue = queue.Queue()
           #         self._results = {}
           #         self._lock = threading.Lock() # For results dict
           #         self._start_worker()
           #
           #     def _start_worker(self):
           #         self._worker = threading.Thread(target=self._process_jobs, daemon=True)
           #         self._worker.start()
           #
           #     def _process_jobs(self):
           #         while True:
           #             try:
           #                 job_id, data = self._job_queue.get()
           #                 # Simulate processing
           #                 result = {"status": "completed", "data": data}
           #                 with self._lock:
           #                     self._results[job_id] = result
           #                 self._job_queue.task_done()
           #             except Exception:
           #                 pass
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._job_queue.put((job_id, data))
           #         return job_id
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         with self._lock:
           #             return self._results.get(job_id)
           # ```
           #
           # This is robust. But is it "basic"?
           # It uses threading.
           # Maybe too complex?
           #
           # Let's reconsider the synchronous approach.
           # If the user just wants to test the logic of a queue data structure.
           #
           # "Implement a basic job queue".
           # Usually, this means the data structure.
           # But the method `get_result` implies state change.
           #
           # Let's look at the constraints again.
           # "Write only the Python code, no explanations."
           #
           # If I provide the threading solution, it works perfectly.
           # If I provide the synchronous solution, it works but might feel like a trick (processing inside add).
           #
           # However, there is a third option.
           # Maybe the class is just a container, and the user is expected to call `process`?
           # But `process` is not in the requirements.
           #
           # Let's stick to the simplest working code that satisfies the text.
           # If I use `collections.deque` and a simple list for results, and process synchronously?
           #
           # Let's try to interpret "processed" as "moved to results".
           # If I don't process it, `get_result` returns None.
           # The example expects a result.
           #
           # Let's write a simple synchronous version. It's safer for a "basic" request without explicit threading requirements.
           # But wait, if I process synchronously, I don't really need a queue data structure, I just need a dict.
           # Why "FIFO order"?
           # If I process synchronously, FIFO is trivial (process 1, then 2).
           #
           # Maybe the requirement "Jobs should be processed in FIFO order" implies that if multiple jobs are added, they must be completed in that order.
           # Synchronous processing guarantees this.
           #
           # Let's refine the synchronous implementation to strictly follow "store jobs internally until processed".
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         # Store job internally
           #         self._queue.append({'id': job_id, 'data': data})
           #
           #         # Process in FIFO order
           #         # Since we just added, we process the head of the queue?
           #         # Or do we process all pending?
           #         # If we process all pending, it's a batch.
           #         # If we process just the one we added, it's immediate.
           #         # But if there was a backlog?
           #         # Synchronous processing usually processes the backlog.
           #
           #         # Let's process all pending jobs to ensure FIFO execution order
           #         # relative to addition.
           #         while self._queue:
           #             job = self._queue.popleft()
           #             self._process(job)
           #
           #         return job_id
           #
           #     def _process(self, job):
           #         # Mock processing
           #         job_id = job['id']
           #         data = job['data']
           #         # Simulate work
           #         result = {"processed": True, "input": data}
           #         self._results[job_id] = result
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # This satisfies:
           # 1. `add_job` stores in queue.
           # 2. Processes in FIFO (deque popleft).
           # 3. Stores internally until processed (popleft removes it, but it was stored).
           # 4. `get_result` works.
           #
           # However, processing inside `add_job` blocks the caller.
           #
           # Let's consider the threading solution again. It's the standard definition of a Job Queue.
           # Is it too complex?
           # "Basic job queue".
           #
           # Let's check if there's a simpler way without threading but with a `process` method?
           # No, requirements are strict on methods.
           #
           # Maybe the prompt implies that the `JobQueue` is just the *queue* part, and the processing is external?
           # But then `get_result` would fail in the example.
           #
           # Let's look at the example usage again.
           # `job_id = queue.add_job(...)`
           # `# After processing...`
           # `result = queue.get_result(...)`
           #
           # If I provide a class where `add_job` just queues, and `get_result` returns None, the example is broken.
           # If I provide a class where `add_job` processes, the example works.
           #
           # Maybe the "processing" is just a time delay?
           #
           # Let's go with a simple synchronous processing that transforms the data. It's the most self-contained solution.
           #
           # Wait, if I process synchronously, the "queue" aspect is hidden.
           #
           # Let's try to find a solution that keeps the queue visible but processes.
           # Maybe `add_job` adds to queue.
           # `get_result` checks results.
           # If result not found, maybe it triggers processing?
           # No, that's lazy evaluation.
           #
           # Let's assume the user is okay with a synchronous execution for a "basic" implementation, or perhaps they just want the data structure and the "processing" comment in the example is just flavor text for "when the job is done".
           #
           # Actually, there is a very simple interpretation:
           # The `JobQueue` is a container.
           # The "processing" is done by the user (implied).
           # But the user needs to *see* the result.
           # Maybe `get_result` is supposed to return the job data if not processed? No, "result of a completed job".
           #
           # Okay, I will implement the threading solution. It is the most correct software engineering answer for "Job Queue". It handles the "After processing" comment naturally. It ensures FIFO. It stores internally.
           #
           # Wait, threading might be considered "advanced" for a "basic" request?
           # But it's standard Python.
           #
           # Let's check if there is a simpler way.
           # Maybe just a list and a dict, and I assume the user calls a `process` method that I should have included but didn't?
           # No, I must follow requirements.
           #
           # Let's look at the requirements again.
           # "Jobs should be processed in FIFO order".
           # This is a requirement for the *system* or the *class*.
           #
           # If I write:
           # ```python
           # class JobQueue:
           #     def __init__(self):
           #         self.jobs = []
           #         self.results = {}
           #     def add_job(self, job_id, data):
           #         self.jobs.append((job_id, data))
           #         return job_id
           #     def get_result(self, job_id):
           #         return self.results.get(job_id)
           # ```
           # This fails the "processed" requirement because nothing processes it.
           #
           # So processing logic is required.
           #
           # Let's try a hybrid.
           # Maybe the processing is simulated by just moving data to results?
           #
           # Let's write a solution that processes synchronously but keeps the queue structure explicit.
           #
           # Actually, looking at the prompt "Implement a basic job queue", and the example.
           # The example shows `add_job` then `get_result`.
           # If I process synchronously, `get_result` works immediately.
           # If I process asynchronously, `get_result` might return None if called too fast.
           # The comment `# After processing...` handles the timing.
           #
           # So asynchronous (threading) is actually safer for the example logic (waiting for result).
           #
           # However, for a "basic" implementation, threading can be tricky (daemon threads, cleanup).
           #
           # Let's consider the possibility that the "processing" is just a mock function I define, and I call it.
           #
           # Let's go with the synchronous approach but structured nicely. It's less error-prone for a code snippet request.
           # Wait, if I process synchronously, I don't need a queue.
           #
           # Let's look at the constraint: "The queue should store jobs internally until they are processed".
           # This implies the job sits in the queue.
           #
           # If I use a thread, the job sits in the queue (thread-safe queue) until the thread picks it up. This fits the description perfectly.
           #
           # Let's write the threading version. It's robust.
           #
           # Wait, I need to import `threading` and `queue`.
           #
           # Let's double check if I can do it without imports?
           # `collections.deque` is standard.
           # Threading is standard.
           #
           # Let's refine the threading code.
           #
           # ```python
           # import threading
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #         self._lock = threading.Lock()
           #         self._stop_event = threading.Event()
           #         self._worker = threading.Thread(target=self._process_loop, daemon=True)
           #         self._worker.start()
           #
           #     def _process_loop(self):
           #         while not self._stop_event.is_set():
           #             with self._lock:
           #                 if self._queue:
           #                     job_id, data = self._queue.popleft()
           #                 else:
           #                     # Wait a bit to avoid busy spinning
           #                     self._stop_event.wait(0.1)
           #                     continue
           #
           #             # Process job
           #             result = self._execute(data)
           #
           #             with self._lock:
           #                 self._results[job_id] = result
           #
           #     def _execute(self, data):
           #         # Basic processing simulation
           #         return {"status": "done", "data": data}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         with self._lock:
           #             self._queue.append((job_id, data))
           #         return job_id
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         with self._lock:
           #             return self._results.get(job_id)
           # ```
           #
           # This is a bit verbose for "basic".
           #
           # Let's try to find the simplest interpretation.
           # Maybe the prompt implies a synchronous execution but wants to see the queue logic?
           #
           # "Jobs should be processed in FIFO order"
           # "The queue should store jobs internally until they are processed"
           #
           # If I just implement a class that stores jobs in a list and has a method to process them, but the method isn't requested...
           #
           # What if `add_job` adds to the queue, and `get_result` *triggers* processing if the result isn't there?
           # No, that changes semantics.
           #
           # Let's assume the prompt is a trick question or a very simple data structure exercise.
           # Maybe "processed" means "moved to a completed list"?
           #
           # Let's look at the example again.
           # `job_id = queue.add_job("job1", {"task": "process_data"})`
           # `# After processing...`
           # `result = queue.get_result("job1")`
           #
           # If I write code that doesn't process, the user gets `None`.
           # If I write code that processes, the user gets a dict.
           #
           # I will provide a solution that processes the job synchronously upon addition, but keeps the queue structure to satisfy the "store internally" and "FIFO" requirements (conceptually).
           # Actually, if I process synchronously, I can just pop from the queue immediately.
           #
           # But wait, if I have multiple jobs, and I call `add_job` multiple times, and each `add_job` processes the queue...
           # 1. Add J1. Queue: [J1]. Process J1. Queue: []. Result: J1.
           # 2. Add J2. Queue: [J2]. Process J2. Queue: []. Result: J2.
           # This works.
           #
           # What if I add J1, then J2, then process?
           # If `add_job` processes immediately, J1 is done before J2 is added.
           # Is that FIFO? Yes.
           #
           # But "store jobs internally until they are processed".
           # In the synchronous case, the storage duration is microseconds.
           #
           # Let's consider the possibility that the user wants a class that *is* a queue, and they will call a `process` method themselves, but they forgot to list it?
           # "Requirements: 1. Create a JobQueue class with the following methods: add_job, get_result".
           # It's an exhaustive list.
           #
           # Okay, I'll stick to the synchronous processing inside `add_job` or a simple mock.
           # Actually, the most "basic" job queue implementation that fits the text without threading is:
           # Store job in a list.
           # When `get_result` is called? No.
           #
           # Maybe the "processing" is just returning the data?
           #
           # Let's try to interpret "processed" as "the job is done".
           #
           # Let's write a simple class that uses a `deque` and processes jobs in a simple loop if called? No.
           #
           # Let's go with the `queue.Queue` (threading) approach. It is the most professional interpretation of "Job Queue". Even if it's "basic", using the standard library `queue` module is the right way.
           #
           # However, for a coding interview snippet, sometimes they want the logic, not the threading overhead.
           #
           # Let's look at the constraints: "Write only the Python code".
           #
           # I'll provide a solution that uses `collections.deque` and simulates processing synchronously for simplicity, as it guarantees the result is available for `get_result` without race conditions or waiting, which fits the "basic" descriptor and the example flow (where result is fetched right after).
           #
           # Wait, if I process synchronously, I don't need to store it in a queue *until* processed. I can just process it.
           # But requirement 3 says "store jobs internally until they are processed".
           # So:
           # 1. Append to deque.
           # 2. Pop from deque (FIFO).
           # 3. Process.
           # 4. Store result.
           #
           # This strictly follows the requirements.
           #
           # Let's refine the code.
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         # Store job internally
           #         self._queue.append({'id': job_id, 'data': data})
           #
           #         # Process jobs in FIFO order
           #         # We process the queue until empty? Or just the new one?
           #         # If we process until empty, it handles backlog.
           #         while self._queue:
           #             job = self._queue.popleft()
           #             self._process_job(job)
           #
           #         return job_id
           #
           #     def _process_job(self, job):
           #         job_id = job['id']
           #         data = job['data']
           #         # Simulate processing logic
           #         # For a basic queue, we can just return the data or a status
           #         result = {"status": "completed", "data": data}
           #         self._results[job_id] = result
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # Is there any downside?
           # - Blocking `add_job`.
           # - Not truly asynchronous.
           # - But satisfies all text requirements literally.
           #
           # Let's check if there's a way to make it non-blocking without threads? No.
           #
           # Let's check if the prompt implies a specific processing logic? No.
           #
           # Let's consider the `queue.Queue` approach again.
           # It's cleaner for "Queue".
           # But requires imports and threading.
           #
           # Let's try to write the `queue.Queue` version. It's safer.
           #
           # ```python
           # import queue
           # import threading
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._job_queue = queue.Queue()
           #         self._results = {}
           #         self._lock = threading.Lock()
           #         self._start_worker()
           #
           #     def _start_worker(self):
           #         self._worker = threading.Thread(target=self._process_jobs, daemon=True)
           #         self._worker.start()
           #
           #     def _process_jobs(self):
           #         while True:
           #             try:
           #                 job_id, data = self._job_queue.get()
           #                 # Process job (mock)
           #                 result = {"status": "completed", "data": data}
           #                 with self._lock:
           #                     self._results[job_id] = result
           #                 self._job_queue.task_done()
           #             except Exception:
           #                 pass
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._job_queue.put((job_id, data))
           #         return job_id
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         with self._lock:
           #             return self._results.get(job_id)
           # ```
           #
           # This is good. But maybe too much code?
           #
           # Let's check the requirements again.
           # "Implement a basic job queue".
           #
           # Maybe the simplest valid code is preferred.
           #
           # What if I just store the job in a dict and assume "processed" means "added"?
           # No, "until they are processed".
           #
           # Let's go with the synchronous processing using `deque`. It's self-contained, no threading imports needed (except maybe not even that, `deque` is in `collections`), and it works perfectly for the example.
           #
           # Wait, `deque` is in `collections`.
           #
           # Let's refine the synchronous code to be clean.
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_queue()
           #         return job_id
           #
           #     def _process_queue(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             self._results[job["id"]] = self._process(job["data"])
           #
           #     def _process(self, data: dict) -> dict:
           #         # Placeholder for actual job processing logic
           #         return {"status": "completed", "input_data": data}
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # This looks solid.
           # - `add_job` adds to queue.
           # - `_process_queue` processes in FIFO (popleft).
           # - Stores result.
           # - `get_result` retrieves.
           #
           # One issue: If I add multiple jobs, `add_job` processes *all* of them.
           # Is that okay?
           # "Jobs should be processed in FIFO order". Yes.
           # "Store jobs internally until they are processed".
           # When `add_job` is called, it stores. Then it processes.
           # Technically it stores *before* processing.
           #
           # But if I call `add_job("j1")`, it stores j1, processes j1.
           # If I call `add_job("j2")`, it stores j2, processes j2.
           #
           # What if I want to add j1, j2, j3, and *then* process?
           # The synchronous approach forces immediate processing.
           #
           # If the user wanted batch processing, they would need a `process` method.
           # Since they didn't ask for it, and asked for `get_result` to work, immediate processing is a safe bet for a "basic" implementation where the user expects a result.
           #
           # However, "Job Queue" usually implies decoupling.
           #
           # Let's try to compromise.
           # Maybe the "processing" is just a mock, and I should just return the data?
           #
           # Let's look at the prompt again.
           # "Implement a basic job queue".
           #
           # I'll stick to the synchronous deque approach. It's the most robust "code snippet" that doesn't rely on external threads which might hang or be complex to read.
           #
           # Wait, if I use `queue.Queue` (threading), it's actually very standard.
           # But `queue.Queue` blocks on `get` if empty. My worker loop handles that.
           #
           # Let's consider the user might copy-paste this.
           # Threading code might be scary or require understanding of daemon threads.
           # Deque code is pure logic.
           #
           # Let's check if there's any ambiguity in "processed".
           # If I just store the job and don't process it, `get_result` returns None.
           # The example implies `get_result` returns something.
           # So processing must happen.
           #
           # I will provide the synchronous solution.
           #
           # Wait, I can make it slightly better.
           # Instead of processing *all* jobs in `add_job` (which might be slow if queue is huge), I can just process the one I added?
           # But if there was a backlog, I should process that first (FIFO).
           # So processing the whole queue is correct for FIFO consistency.
           #
           # Actually, if I process the whole queue, the order of completion is strictly FIFO relative to addition.
           #
           # Let's write the code.
           #
           # Wait, I should check if `dict | None` syntax is supported. It's Python 3.10+.
           # The prompt uses `dict | None` in the signature. So I should assume modern Python.
           #
           # Let's refine the code to be clean.
           #
           # ```python
           # from collections import deque
           # from typing import Dict, Optional, Any
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue: deque = deque()
           #         self._results: Dict[str, Dict] = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_pending_jobs()
           #         return job_id
           #
           #     def _process_pending_jobs(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             job_id = job["id"]
           #             data = job["data"]
           #             # Simulate processing
           #             result = {"status": "completed", "data": data}
           #             self._results[job_id] = result
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # This seems complete.
           #
           # One detail: "The queue should store jobs internally until they are processed".
           # In my code, the job is in `self._queue` until `popleft`.
           # So it is stored internally.
           #
           # Is there any risk?
           # If `add_job` is called, it blocks until processing is done.
           # This is synchronous.
           #
           # Is there a way to do this without blocking?
           # Only with threads.
           #
           # Let's consider if the prompt allows for a non-blocking queue where `get_result` might return None initially?
           # The example:
           # `job_id = queue.add_job(...)`
           # `# After processing...`
           # `result = queue.get_result(...)`
           #
           # The comment `# After processing...` is the key. It implies time passes or an action happens.
           # If I block in `add_job`, the "After processing" comment is technically true (processing happened before the comment line executed), but it feels like a trick.
           #
           # However, without a `process` method, blocking in `add_job` is the only way to guarantee the result exists for `get_result` in a single-threaded context.
           #
           # UNLESS... the user is expected to call `add_job` multiple times, and then maybe there's an implicit processing?
           # No.
           #
           # Let's consider the threading solution again.
           # It respects the "After processing" comment better (processing happens in background).
           # And it's a "Job Queue".
           #
           # Let's write the threading solution. It's more correct.
           #
           # ```python
           # import threading
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #         self._lock = threading.Lock()
           #         self._start_worker()
           #
           #     def _start_worker(self):
           #         self._worker = threading.Thread(target=self._process_loop, daemon=True)
           #         self._worker.start()
           #
           #     def _process_loop(self):
           #         while True:
           #             with self._lock:
           #                 if self._queue:
           #                     job = self._queue.popleft()
           #                 else:
           #                     # Simple wait to prevent CPU spinning
           #                     # In a real scenario, use a Condition or Queue
           #                     import time
           #                     time.sleep(0.01)
           #                     continue
           #
           #             job_id = job['id']
           #             data = job['data']
           #
           #             # Process job
           #             result = {"status": "completed", "data": data}
           #
           #             with self._lock:
           #                 self._results[job_id] = result
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         with self._lock:
           #             self._queue.append({'id': job_id, 'data': data})
           #         return job_id
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         with self._lock:
           #             return self._results.get(job_id)
           # ```
           #
           # This is a bit messy with the lock and sleep.
           # Using `queue.Queue` is much cleaner.
           #
           # ```python
           # import queue
           # import threading
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._job_queue = queue.Queue()
           #         self._results = {}
           #         self._lock = threading.Lock()
           #         self._start_worker()
           #
           #     def _start_worker(self):
           #         self._worker = threading.Thread(target=self._process_jobs, daemon=True)
           #         self._worker.start()
           #
           #     def _process_jobs(self):
           #         while True:
           #             try:
           #                 job_id, data = self._job_queue.get()
           #                 # Simulate processing
           #                 result = {"status": "completed", "data": data}
           #                 with self._lock:
           #                     self._results[job_id] = result
           #                 self._job_queue.task_done()
           #             except Exception:
           #                 pass
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._job_queue.put((job_id, data))
           #         return job_id
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         with self._lock:
           #             return self._results.get(job_id)
           # ```
           #
           # This is very clean.
           #
           # But is it "basic"?
           # It uses imports.
           #
           # Let's think if there's a simpler way without threading that satisfies the "After processing" comment.
           # Maybe the user just wants the structure, and the comment is just illustrative?
           #
           # Let's look at the requirements one last time.
           # 1. `add_job`
           # 2. `get_result`
           # 3. FIFO
           # 4. Store internally until processed.
           #
           # If I implement the synchronous version, it's 100% compliant with text, but maybe not spirit of "Queue" (decoupling).
           # If I implement threading, it's compliant with spirit, but maybe overkill.
           #
           # However, the prompt is "Implement a basic job queue".
           # In Python, a basic job queue often implies `queue.Queue`.
           #
           # But wait, `queue.Queue` is a module.
           #
           # Let's try to write a version that doesn't use threads but allows for the "After processing" gap?
           # Impossible without threads or async.
           #
           # So it's either synchronous processing (blocking) or threading.
           #
           # Given the constraints of a chat response, a synchronous solution is easier to read and debug for a "basic" request. The threading solution introduces concepts like daemon threads and locks which might distract from the core logic of the queue (FIFO, storage).
           #
           # Also, the synchronous solution guarantees that `get_result` works immediately after `add_job` (conceptually), which might be what the user expects if they don't understand concurrency.
           #
           # But the comment `# After processing...` is specific.
           #
           # Let's check if there is a middle ground.
           # Maybe a simple list and a `process` method?
           # But `process` is not allowed.
           #
           # I will provide the synchronous solution. It is the most robust "code snippet" that works without external dependencies or concurrency issues.
           #
           # Wait, if I process synchronously, I don't need `deque`. A list is fine.
           # But `deque` is better for FIFO.
           #
           # Let's refine the synchronous code.
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_jobs()
           #         return job_id
           #
           #     def _process_jobs(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             job_id = job["id"]
           #             data = job["data"]
           #             # Basic processing: return a result dict
           #             result = {"status": "completed", "data": data}
           #             self._results[job_id] = result
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # This is clean.
           #
           # Wait, if I have a huge queue, `add_job` hangs.
           # But for a "basic" implementation, it's acceptable.
           #
           # Actually, let's look at the requirement: "The queue should store jobs internally until they are processed".
           # If I process immediately, the storage time is negligible.
           #
           # Maybe I should separate the storage and processing?
           # But I have no trigger for processing.
           #
           # What if I interpret "processed" as "retrieved"?
           # No, "result of a completed job".
           #
           # Okay, I'll stick to the synchronous processing. It's the safest bet for a code generation task where the user might just run the code and expect it to work. Threading might fail silently or require `time.sleep` in the example to work.
           #
           # Wait, the example has `# After processing...`.
           # If I run the synchronous code:
           # 1. `add_job` -> processes -> returns.
           # 2. Comment.
           # 3. `get_result` -> returns result.
           # It works.
           #
           # If I run the threaded code:
           # 1. `add_job` -> puts in queue -> returns.
           # 2. Comment.
           # 3. `get_result` -> might return None if worker hasn't run yet.
           # The user would see `None` and think it's broken.
           # Unless they wait.
           #
           # So synchronous is actually better for a "basic" demo unless specified otherwise.
           #
           # But wait, "Jobs should be processed in FIFO order".
           # If I process synchronously in `add_job`, I am processing in the order they are added.
           #
           # Let's verify the "store internally" part.
           # `self._queue.append(...)` stores it.
           # Then `popleft` removes it.
           # So it was stored internally.
           #
           # I'll go with this.
           #
           # One optimization:
           # Instead of processing *all* pending jobs in `add_job` (which might be inefficient if queue is large), I could just process the one I added?
           # But if there's a backlog, I must process backlog first for FIFO.
           # So processing the whole queue is correct.
           #
           # Actually, if I just process the one I added, and there was a backlog, the backlog stays.
           # But since `add_job` is the only entry point, and it processes, the queue stays empty (or processes everything).
           #
           # Let's write the code.
           #
           # Wait, I can make it slightly more "queue-like" by not processing immediately, but having a `process` method?
           # No, not allowed.
           #
           # Okay, code time.
           #
           # I will use `collections.deque`.
           # I will use a simple mock processing.
           #
           # One detail: The prompt asks for `dict | None` return type.
           # I should use type hints.
           #
           # ```python
           # from collections import deque
           # from typing import Dict, Optional
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue: deque = deque()
           #         self._results: Dict[str, Dict] = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_queue()
           #         return job_id
           #
           #     def _process_queue(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             job_id = job["id"]
           #             data = job["data"]
           #             # Simulate processing
           #             result = {"status": "completed", "data": data}
           #             self._results[job_id] = result
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # Wait, `dict | None` syntax is Python 3.10.
           # The prompt uses it.
           # So I can use it.
           #
           # Let's double check if I should process *only* the new job or *all*?
           # If I add J1. Queue: [J1]. Process J1. Queue: [].
           # If I add J2. Queue: [J2]. Process J2. Queue: [].
           #
           # What if I add J1, J2, J3 in a loop?
           # 1. Add J1. Process J1.
           # 2. Add J2. Process J2.
           # 3. Add J3. Process J3.
           # Order is preserved.
           #
           # What if I want to add J1, J2, J3 and *then* process?
           # With this implementation, they are processed one by one as added.
           # This is technically FIFO processing.
           #
           # But "store jobs internally until they are processed".
           # If I process J1 immediately, it wasn't stored for long.
           #
           # Maybe I should just store them and have a method to process?
           # But I can't add methods.
           #
           # Maybe the "processing" is just a state change?
           #
           # Let's consider the threading solution again.
           # It allows J1, J2, J3 to be added (stored) and then processed in background.
           # This fits "store internally until processed" much better.
           #
           # And it fits "After processing..." comment better.
           #
           # I'll provide the threading solution. It's the correct answer for "Job Queue".
           # I'll keep it simple.
           #
           # ```python
           # import threading
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #         self._lock = threading.Lock()
           #         self._start_worker()
           #
           #     def _start_worker(self):
           #         self._worker = threading.Thread(target=self._process_loop, daemon=True)
           #         self._worker.start()
           #
           #     def _process_loop(self):
           #         while True:
           #             with self._lock:
           #                 if self._queue:
           #                     job = self._queue.popleft()
           #                 else:
           #                     # Wait to avoid busy loop
           #                     import time
           #                     time.sleep(0.01)
           #                     continue
           #
           #             job_id = job['id']
           #             data = job['data']
           #
           #             # Process job
           #             result = {"status": "completed", "data": data}
           #
           #             with self._lock:
           #                 self._results[job_id] = result
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         with self._lock:
           #             self._queue.append({'id': job_id, 'data': data})
           #         return job_id
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         with self._lock:
           #             return self._results.get(job_id)
           # ```
           #
           # This is a bit heavy.
           #
           # Let's try a very simple synchronous solution but structured to look like a queue.
           # Actually, the synchronous solution is safer for a generic "write code" prompt. Threading can be flaky in some environments or require `time` imports.
           #
           # Let's look at the requirements again.
           # "Jobs should be processed in FIFO order".
           # "The queue should store jobs internally until they are processed".
           #
           # If I use the synchronous approach:
           # 1. `add_job` appends to deque. (Stored internally)
           # 2. `add_job` pops from deque. (FIFO)
           # 3. Processes.
           # 4. Stores result.
           #
           # This satisfies all constraints literally.
           #
           # Let's check if there's any ambiguity.
           # "Store jobs internally until they are processed".
           # If I append and immediately pop, is it "stored until processed"?
           # Yes, it is in the storage structure (deque) before processing starts.
           #
           # I will provide the synchronous solution. It's concise and correct.
           #
           # Wait, I can make it even simpler.
           # Just use a list.
           #
           # ```python
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = []
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_jobs()
           #         return job_id
           #
           #     def _process_jobs(self):
           #         while self._queue:
           #             job = self._queue.pop(0) # FIFO
           #             self._results[job["id"]] = {"status": "completed", "data": job["data"]}
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # `pop(0)` is O(N). `deque` is O(1).
           # I'll use `deque`.
           #
           # Final check on requirements.
           # 1. `JobQueue` class.
           # 2. `add_job` returns `job_id`.
           # 3. `get_result` returns `dict | None`.
           # 4. FIFO.
           # 5. Store internally.
           # 6. Unique job_id (handled by user passing it, or I could generate it, but signature takes `job_id`).
           #
           # The signature `add_job(job_id: str, ...)` implies the caller provides the ID.
           #
           # I'll stick to the synchronous `deque` implementation. It's the most robust "basic" implementation.
           #
           # Wait, if I process synchronously, the example `# After processing...` is slightly misleading but functionally correct (processing happened).
           #
           # Let's consider if the prompt implies a mock for "processing".
           # "process_data" in the example data.
           # Maybe I should just return the data?
           #
           # I'll return a dict with status.
           #
           # Let's write the final code.
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_queue()
           #         return job_id
           #
           #     def _process_queue(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             job_id = job["id"]
           #             data = job["data"]
           #             # Simulate processing
           #             result = {"status": "completed", "data": data}
           #             self._results[job_id] = result
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # This looks good.
           #
           # Wait, is there any chance "processed" means something else?
           # No.
           #
           # One edge case: `get_result` before processing?
           # In synchronous mode, it's impossible (unless exception).
           # In async mode, it returns None.
           # The prompt doesn't specify behavior for unprocessed jobs, but `dict | None` suggests None is valid.
           #
           # If I use synchronous, `get_result` will always find the result (assuming no crash).
           #
           # I think the synchronous solution is the intended "basic" solution.
           #
           # Let's check if I should handle duplicate job_ids?
           # "Each job has a unique job_id".
           # I can assume valid input or overwrite. Overwriting is standard for dicts.
           #
           # Final Code Structure:
           # Imports.
           # Class.
           # Methods.
           #
           # I will use `typing` for clarity if needed, but the prompt used `dict | None` which is runtime compatible in 3.10+. I'll stick to standard types.
           #
           # Actually, to be safe and "basic", I won't over-engineer.
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_jobs()
           #         return job_id
           #
           #     def _process_jobs(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             # Simulate processing logic
           #             result = {"status": "completed", "data": job["data"]}
           #             self._results[job["id"]] = result
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # Wait, if I process *all* jobs in `_process_jobs`, and I call `add_job` multiple times, it processes the whole queue every time.
           # 1. Add J1. Queue [J1]. Process J1. Queue [].
           # 2. Add J2. Queue [J2]. Process J2. Queue [].
           #
           # This is fine.
           #
           # What if I want to batch add?
           # `q.add_job("1", ...)` -> processes 1.
           # `q.add_job("2", ...)` -> processes 2.
           #
           # It works.
           #
           # What if I just want to add to queue without processing?
           # Not supported by this design, but requirements don't ask for it.
           #
           # I'll stick to this.
           #
           # Wait, I can make it slightly more efficient.
           # Only process if queue is not empty? It is checked in while loop.
           #
           # Actually, if I just process the one I added, it's faster.
           # But if there's a backlog, I must process backlog first.
           # Since `add_job` is the only way to add, and it processes, backlog is always empty before new job added?
           # Yes, because previous `add_job` cleared it.
           # So I only need to process the current job.
           #
           # But to be strictly FIFO compliant with the "queue" concept (handling backlog), processing the whole queue is safer logic-wise, even if performance is same here.
           #
           # Let's refine the code to be minimal and clean.
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_queue()
           #         return job_id
           #
           #     def _process_queue(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             self._results[job["id"]] = {"status": "completed", "data": job["data"]}
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # This is good.
           #
           # One thought: The prompt example `# After processing...` might imply that processing takes time.
           # If I process synchronously, it's instant.
           # But without threading, I can't simulate time easily without `time.sleep`.
           # I'll leave it instant.
           #
           # Let's check if the user might want the queue to just store jobs and I should implement a `process` method?
           # "Requirements: 1. Create a JobQueue class with the following methods: add_job, get_result".
           # It's an exclusive list.
           #
           # So processing must be internal.
           #
           # I'll provide the synchronous solution.
           #
           # Wait, I can make it non-blocking and simple?
           # No.
           #
           # I'll provide the code now.
           #
           # Wait, I should check if `dict | None` is valid syntax for the return type hint in the method signature in the code I write?
           # Yes, in Python 3.10+.
           # If the environment is older, it might crash.
           # But the prompt used it in the description.
           # So I should use it.
           #
           # However, to be safe, I can use `Optional[dict]` from typing, or just `dict | None`.
           # I'll use `dict | None` as requested.
           #
           # One detail: The prompt says "Write only the Python code".
           #
           # I will include the import.
           #
           # Final check:
           # - Class `JobQueue`
           # - `add_job` takes `job_id`, `data`. Returns `job_id`.
           # - `get_result` takes `job_id`. Returns `dict | None`.
           # - FIFO: `deque` popleft.
           # - Store internally: `deque` append.
           # - Processed: Logic inside.
           #
           # Looks good.
           #
           # Wait, if I process synchronously, I am technically not storing it "until" processed in a meaningful way (it's stored for 0 seconds).
           # But technically true.
           #
           # Is there any interpretation where I don't process immediately?
           # Only if I have a worker.
           #
           # Let's write the worker version?
           # It's more "correct" for a Job Queue.
           # But it's more code.
           #
           # Let's look at the complexity.
           # Synchronous: 15 lines.
           # Threading: 30+ lines.
           #
           # "Basic job queue".
           # Synchronous is basic.
           #
           # I'll go with synchronous.
           #
           # Wait, if I use synchronous, `add_job` blocks.
           # If `data` processing is heavy, it blocks.
           # But `data` is just a dict.
           #
           # Okay, I'm overthinking. The synchronous solution is the standard "mock" implementation for such prompts unless "concurrent" or "async" is specified.
           #
           # Let's refine the processing logic to be slightly more realistic?
           # Just returning the data is fine.
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_jobs()
           #         return job_id
           #
           #     def _process_jobs(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             # Simulate processing
           #             result = {"status": "completed", "data": job["data"]}
           #             self._results[job["id"]] = result
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # Wait, if I call `add_job` twice rapidly?
           # 1. Add J1. Process J1.
           # 2. Add J2. Process J2.
           #
           # If I wanted to batch process, I would need a `process` method.
           #
           # I'll stick to this.
           #
           # Actually, looking at the code, `_process_jobs` processes *all* pending jobs.
           # If I add J1, it processes J1.
           # If I add J2, it processes J2.
           #
           # It works.
           #
           # One small thing: The prompt says "Jobs should be processed in FIFO order".
           # If I process immediately, the order of processing is the order of addition.
           # So it is FIFO.
           #
           # I'll output this.
           #
           # Wait, I can make it even simpler.
           # Just store in a list and process?
           # `deque` is better practice.
           #
           # I'll use `deque`.
           #
           # One final check: The prompt example `job_id = queue.add_job("job1", {"task": "process_data"})`.
           # My code returns `job_id` ("job1"). Correct.
           # `result = queue.get_result("job1")`.
           # My code returns `{"status": "completed", "data": {"task": "process_data"}}`.
           # This is a dict. Correct.
           #
           # If job not found, returns None. Correct.
           #
           # I'll write the code now.
           #
           # Wait, I should probably not process *all* jobs in `add_job` if I want to strictly adhere to "store internally until processed" in a way that implies a queue buffer.
           # But without a trigger, I can't buffer.
           #
           # Maybe I should just store it and assume the user knows?
           # No, `get_result` needs data.
           #
           # I'll stick to the synchronous processing. It's the only way to make `get_result` work without external triggers.
           #
           # Wait, maybe I can process lazily in `get_result`?
           # If result not in dict, process queue until found?
           #
           # ```python
           # def get_result(self, job_id: str) -> dict | None:
           #     if job_id not in self._results:
           #         self._process_until(job_id)
           #     return self._results.get(job_id)
           #
           # def _process_until(self, target_id):
           #     while self._queue:
           #         job = self._queue.popleft()
           #         # process
           #         self._results[job["id"]] = ...
           #         if job["id"] == target_id:
           #             break
           # ```
           #
           # This is interesting. It processes jobs on demand.
           # But it processes in FIFO order.
           # And it stores internally.
           # And it doesn't block `add_job`.
           #
           # But if I ask for result of J2, it processes J1 and J2.
           # If I ask for J1, it processes J1.
           #
           # This seems like a valid "lazy" queue implementation.
           # But it's more complex logic.
           #
           # The synchronous `add_job` is simpler.
           #
           # I'll stick to the synchronous `add_job` processing.
           #
           # Actually, the simplest "Job Queue" that works is:
           # 1. Add to list.
           # 2. When `get_result` called, if not done, process?
           # No, that's lazy.
           #
           # I'll provide the synchronous processing in `add_job`.
           #
           # Wait, if I process in `add_job`, I don't really need a queue structure, just a dict.
           # But I used a deque to satisfy "store internally" and "FIFO".
           #
           # Okay, ready.
           #
           # One detail: `dict | None` syntax.
           # I will use it.
           #
           # ```python
           # from collections import deque
           #
           # class JobQueue:
           #     def __init__(self):
           #         self._queue = deque()
           #         self._results = {}
           #
           #     def add_job(self, job_id: str, data: dict) -> str:
           #         self._queue.append({"id": job_id, "data": data})
           #         self._process_queue()
           #         return job_id
           #
           #     def _process_queue(self):
           #         while self._queue:
           #             job = self._queue.popleft()
           #             self._results[job["id"]] = {"status": "completed", "data": job["data"]}
           #
           #     def get_result(self, job_id: str) -> dict | None:
           #         return self._results.get(job_id)
           # ```
           #
           # This is the code.
           #
           # Wait, I can make `_process_queue` process only the head?
           # No, if I have a backlog, I should clear it.
           # But since `add_job` is the only entry, backlog is empty.
           # So processing just the head (which is the only item) is sufficient.
           # But `while` loop is safer.
           #
           # I'll keep the `while` loop.
           #
           # Actually, to be strictly "FIFO", if I add J1, J2, J3 without processing (if I had a way), they would be processed 1, 2, 3.
           # My code processes 1, then 2, then 3.
           # Order is preserved.
           #
           # Final check on imports. `deque` is in `collections`.
           #
           # I'll output the code.

</reasoning_content>

<content>
```python
from collections import deque

class JobQueue:
    def __init__(self):
        self._queue = deque()
        self._results = {}

    def add_job(self, job_id: str, data: dict) -> str:
        self._queue.append({"id": job_id, "data": data})
        self._process_queue()
        return job_id

    def _process_queue(self):
        while self._queue:
            job = self._queue.popleft()
            job_id = job["id"]
            data = job["data"]
            # Simulate processing logic
            result = {"status": "completed", "data": data}
            self._results[job_id] = result

    def get_result(self, job_id: str) -> dict | None:
        return self._results.get(job_id)
```
</content>