<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.
    *   Behavior:
        *   FIFO order.
        *   Store jobs internally.
        *   Unique `job_id`.
    *   Constraint: Write *only* the Python code, no explanations.

2.  **Determine Internal Structure:**
    *   To support FIFO, a `collections.deque` is appropriate for the processing queue.
    *   To store results (since `get_result` implies jobs finish and results are available later), a dictionary mapping `job_id` to `result` is needed.
    *   Since the prompt implies a basic implementation and doesn't specify threading/async processing logic, I need to simulate "processing" or just store the data. However, `get_result` usually implies a completed state.
    *   *Self-Correction/Refinement:* The prompt asks for a "basic job queue". Usually, a queue holds items waiting to be processed. If I just store them, `get_result` might return `None` until something processes them.
    *   However, without a specific "process" method or a background thread defined in the requirements, the simplest interpretation is a storage mechanism where jobs are added and eventually results are looked up.
    *   Let's look at the example:
        ```python
        queue = JobQueue()
        job_id = queue.add_job("job1", {"task": "process_data"})
        # After processing...
        result = queue.get_result("job1")
        ```
    *   The comment `# After processing...` implies time passes or an external system processes it, or perhaps the class needs a method to process. But the requirements *only* list `add_job` and `get_result`.
    *   If I strictly follow requirements 1-4, I need to store jobs. If `get_result` is called before processing, it should probably return `None` or the data itself if it's synchronous.
    *   Let's assume a standard pattern:
        1.  `pending_jobs`: deque of (job_id, data).
        2.  `results`: dict of job_id -> result.
        3.  Since there's no `process` method required, maybe `add_job` just stores it, and `get_result` checks if it's done? But nothing marks it done.
    *   *Alternative Interpretation:* The prompt might be asking for a structure where `add_job` puts it in a queue, and perhaps `get_result` blocks? No, that's complex for "basic".
    *   *Most likely interpretation for a coding interview snippet:* It's a wrapper around a data structure. Maybe it simulates processing immediately? Or maybe it just stores the data and `get_result` returns the data (assuming the "result" is the data passed in, or the user fills in the logic).
    *   *Wait, looking at the signature:* `add_job(job_id, data)`. `get_result(job_id)`.
    *   If I don't have a way to mark a job as "completed with result X", `get_result` can't return anything meaningful other than the input data or None.
    *   Let's assume the "result" is the `data` passed in, or the class needs a way to set the result. But no `set_result` method is requested.
    *   Let's look at the constraints again. "Jobs should be processed in FIFO order". This implies there is a processing mechanism. But it's not listed in the methods.
    *   Maybe `add_job` adds to a queue, and `get_result` pops from the queue? No, `get_result` takes a specific ID.
    *   Maybe the class is just a storage container for a queue system.
    *   *Decision:* I will implement a class that stores jobs in a deque (for FIFO) and a dictionary for results. Since there is no explicit processing logic requested, I will assume `add_job` stores the job, and for the sake of the example working, I'll perhaps simulate that the "result" is the data itself, or simply store the job in a "pending" state and a "completed" state.
    *   *Actually*, a very common pattern for these specific constraints (often found in basic backend tasks) is that `add_job` puts it in a queue, and maybe there's a hidden assumption that the user calls a process method, OR, the `get_result` is meant to retrieve the *input data* if it's just a queue, or the prompt implies a synchronous execution.
    *   *Let's stick to the simplest valid implementation:*
        *   Store jobs in a list/deque.
        *   Store results in a dict.
        *   Since I can't process jobs without a method, I'll assume the "result" is the `data` passed in (or None if not processed). But wait, "Jobs should be processed".
    *   Let's look at the prompt again carefully. "Implement a basic job queue". "Jobs should be processed in FIFO order".
    *   If I cannot add a method to process them, maybe `add_job` triggers processing?
    *   Let's try to make it functional based on the example. The example shows `add_job` then `get_result`. If `get_result` returns the data, it works.
    *   However, usually, a JobQueue has a `process` method or runs in a thread. Without it, "FIFO processing" is just a data structure requirement.
    *   Let's implement it with a `deque` for the queue and a `dict` for results. To satisfy "Jobs should be processed", I will add a private method or logic that moves items from queue to results, or just assume the prompt implies a synchronous execution model where `add_job` processes immediately?
    *   No, that defeats the purpose of a queue.
    *   Let's assume the prompt allows for a `process` method even if not explicitly listed, or implies that `get_result` waits? No, `get_result` returns `dict | None`.
    *   *Hypothesis:* The user wants a class that manages the state.
    *   Let's implement:
        1.  `self.queue`: deque of jobs.
        2.  `self.results`: dict.
        3.  `add_job`: appends to queue.
        4.  `get_result`: checks `self.results`.
        5.  *Missing piece:* How do jobs move from queue to results?
        6.  Maybe the prompt implies I should write a `process` method? "Requirements: 1. Create a JobQueue class with the following methods...". It lists specific methods. It doesn't forbid others, but usually, strict prompts want exactly what is asked.
    *   *Alternative:* Maybe `add_job` is synchronous? "Add a job to the queue". If it's synchronous, it processes immediately.
    *   *Alternative:* Maybe `get_result` processes pending jobs in FIFO order until it finds the requested job?
    *   Let's go with the interpretation that `get_result` might trigger processing of the queue up to that job, or simply that the class is a container.
    *   *Actually*, looking at standard "basic job queue" interview questions, often they just want the data structures.
    *   Let's write a class that stores jobs. To make `get_result` return something useful in the example, I will assume the result is the `data` passed in, or I will implement a simple synchronous processing step inside `add_job` or `get_result`.
    *   *Refined Plan:*
        *   Class `JobQueue`.
        *   `__init__`: Initialize `self._queue` (deque) and `self._results` (dict).
        *   `add_job`: Append `(job_id, data)` to `self._queue`. Return `job_id`.
        *   `get_result`: Check `self._results`. If not found, maybe process the queue?
        *   If I process the queue inside `get_result`, it satisfies FIFO.
        *   Processing logic: Pop from `self._queue`, compute result (let's say result = data for simplicity, or just store it), put in `self._results`.
        *   This seems like a robust interpretation of "Jobs should be processed in FIFO order" combined with the limited API.

    *   *Wait*, if `get_result("job1")` is called, and "job1" is at the front, it processes. If "job2" was added after "job1", and I call `get_result("job2")`, should it process "job1" first? Yes, FIFO.
    *   So `get_result` could iterate through the queue, processing items until it finds the requested ID or the queue is empty.
    *   However, usually, a queue is processed by a worker, not the getter.
    *   Let's look at the prompt constraints again. It's very minimal.
    *   Maybe it's just a storage class.
    *   Let's stick to the simplest interpretation: A class that holds jobs. If the user wants "processing", they might expect a `process` method, but since it's not requested, I will implement a simple synchronous processing or just storage.
    *   *Actually*, the most standard "Basic Job Queue" implementation in Python for a snippet like this usually involves `queue.Queue` or `collections.deque`.
    *   Let's assume the prompt allows me to add a `process` method or the logic is implicit. But strict adherence suggests I should only implement what's asked or make it work as described.
    *   If I just store them, `get_result` returns `None` (or the data).
    *   Let's check the Example Usage again.
        ```python
        queue = JobQueue()
        job_id = queue.add_job("job1", {"task": "process_data"})
        # After processing...
        result = queue.get_result("job1")
        ```
    *   The comment `# After processing...` is the key. It implies an external step or a time gap. Since I can't write external code, the class must support the state where a job is added, then later retrieved.
    *   If I don't have a mechanism to mark it "processed" with a specific result, `get_result` can only return the input data or `None`.
    *   Let's assume the "result" is the `data` dictionary, perhaps modified? No, that's guessing.
    *   Let's assume the class is just a wrapper.
    *   *Wait*, maybe I should use `queue.Queue`?
    *   Let's write a clean implementation using `collections.deque` for the queue and a dict for results. I will add a `process` method just in case, or handle it such that it's usable.
    *   *Correction*: The prompt says "Requirements: 1. Create a JobQueue class with the following methods...". It does not explicitly forbid other methods, but it's safer to stick to the interface.
    *   However, without a way to *set* the result of a job (other than the input data), `get_result` is trivial.
    *   Maybe the "processing" is implicit in the prompt's context (e.g., this is part of a larger system).
    *   I will implement it such that `add_job` stores the job, and `get_result` retrieves it. To satisfy "FIFO", I'll use a deque. To satisfy "processed", I'll assume that if a job is in the queue, it's pending, and maybe `get_result` shouldn't return it until it's "processed". But without a process method, it's stuck.
    *   *Pivot*: Maybe `add_job` processes it immediately? No, "Add a job to the queue".
    *   Maybe the prompt implies a threaded queue?
    *   Let's look for a standard interpretation. A "Job Queue" usually holds tasks.
    *   I'll implement a class that stores jobs in a deque. I'll also include a dictionary to store results. I'll add a dummy processing step or just return the data.
    *   *Actually*, looking at the prompt "Jobs should be processed in FIFO order", this is a behavioral requirement.
    *   If I write code that *doesn't* process jobs, I fail requirement 3.
    *   If I write code that processes jobs immediately in `add_job`, I fail "store jobs internally until they are processed" (requirement 3 says "store... until processed", implying a delay/state).
    *   So there must be a state transition.
    *   Since no method is provided to trigger the transition, maybe `get_result` triggers it?
    *   Or maybe the prompt is incomplete and expects me to add a `process` method?
    *   "Write only the Python code".
    *   I will add a `process` method? No, requirements list specific methods.
    *   Okay, I will implement `add_job` to put items in a deque. I will implement `get_result` to check a results dict.
    *   To make it functional for the example, I'll assume the user might call a method not listed, OR I will implement a background thread? No, too complex.
    *   Let's assume the simplest case: The prompt is a test of data structures.
    *   I will store jobs in a `deque`. I will store results in a `dict`.
    *   Since I cannot fulfill "process" without a method, I will assume the "processing" is conceptual or I will add a `process` method despite it not being in the list, or I will make `add_job` synchronous?
    *   Let's look at the wording: "Jobs should be processed in FIFO order". This is a constraint on *how* they are handled.
    *   If I just store them, they aren't processed.
    *   Maybe `get_result` processes the queue?
    *   Let's try this logic:
        1.  `add_job`: appends to `self.queue`.
        2.  `get_result`:
            *   If `job_id` in `self.results`, return it.
            *   While `self.queue` is not empty:
                *   Pop item from `self.queue`.
                *   "Process" it (simulate result).
                *   Store in `self.results`.
                *   If popped item is `job_id`, break (or continue to ensure FIFO for others? No, just need the specific one).
            *   Return result if found.
    *   This satisfies FIFO processing (we process the queue head first) and allows `get_result` to work.
    *   However, usually `get_result` shouldn't side-effect process the whole queue.
    *   *Alternative*: The prompt might be expecting a standard `queue.Queue` usage where `add_job` is `put` and `get_result` is `get`? But `get` blocks or takes an item, it doesn't take an ID.
    *   Let's step back. This looks like a specific coding test question.
    *   Often, these questions allow you to add helper methods.
    *   I'll add a `process` method? No, strict adherence to "methods: add_job, get_result".
    *   Maybe the class *is* the processor?
    *   Let's go with the interpretation that `add_job` adds to a queue, and `get_result` retrieves from a completed store. To make it work, I'll assume a synchronous execution for the sake of the snippet, or just storage.
    *   *Actually*, the most robust "basic job queue" that fits the description without external dependencies is:
        *   `self._jobs`: deque of dicts `{id, data, status}`.
        *   `add_job`: append.
        *   `get_result`: search for completed job.
    *   But nothing marks it completed.
    *   *Let's check the prompt constraints one last time.*
    *   "Implement a basic job queue".
    *   "Jobs should be processed in FIFO order".
    *   "Store jobs internally until they are processed".
    *   Maybe I should use `multiprocessing` or `threading`? "Basic" usually implies single thread or simple logic.
    *   Let's assume the prompt implies that the *user* of the class will handle processing, but the class must support the structure.
    *   But `get_result` needs to return a result.
    *   Okay, I will implement a class that stores the jobs. I will assume that for the purpose of the code snippet, the "result" is the data passed in (or a processed version of it).
    *   To satisfy "processed in FIFO", I will implement a method `process_next` or similar? No, not allowed.
    *   I will interpret "Jobs should be processed in FIFO order" as a requirement for the internal storage order (Deque).
    *   And "store jobs internally until they are processed" means they stay in the deque.
    *   I'll implement `get_result` to look up the job. If it's not "processed" (maybe tracked by a set), it returns None?
    *   But how does it get processed?
    *   Maybe `add_job` processes it? No.
    *   Maybe `get_result` processes it?
    *   Let's write a solution that uses a `deque` for the queue and a `dict` for results. I'll add a simple synchronous processing step in `add_job` or just return the data, as that's the only way to make the example code return a value without extra methods.
    *   *Wait*, if I return the data in `get_result`, it works.
    *   Let's refine:
        *   `add_job`: stores job in a deque (FIFO).
        *   `get_result`: checks if job is in a `results` dict. If not, maybe it processes the queue?
        *   If I process the queue in `get_result`, it's a bit weird but functional.
    *   *Better approach*: The prompt might be a trick or simple. Just use a dictionary for storage? No, "FIFO".
    *   Let's use `collections.deque`.
    *   Let's assume the prompt allows a `process` method or implies synchronous behavior.
    *   Actually, I'll provide a class that has a `process` method, even if not explicitly requested, because "Jobs should be processed" is a requirement. If I don't provide a way to process them, the requirement is impossible to meet.
    *   *Self-Correction*: The prompt says "Create a JobQueue class with the following methods...". It lists specific methods. It does *not* say "and no other methods". It lists requirements. Requirement 3 says "Jobs should be processed...". This implies functionality.
    *   However, usually, if a method isn't listed, you shouldn't rely on it being called by the user, but the class might have internal workers.
    *   Let's stick to the simplest valid code.
    *   I'll use a `deque` for the queue.
    *   I'll use a `dict` for results.
    *   I'll assume that `add_job` just queues it.
    *   I'll assume that `get_result` retrieves it.
    *   To make `get_result` return a result, I'll assume the job is processed immediately upon addition for this basic implementation, or I'll just return the data.
    *   *Wait*, "store jobs internally until they are processed". This implies they are NOT processed immediately.
    *   Okay, there is a missing link. How are they processed?
    *   Maybe the prompt expects me to use `queue.Queue` and `get`?
    *   Let's look at the example usage again.
    *   `queue.add_job(...)`
    *   `# After processing...`
    *   `queue.get_result(...)`
    *   The comment suggests that processing happens *between* these lines, possibly by code not shown, or the class handles it.
    *   If the class handles it, it needs a mechanism.
    *   If I cannot add a method, maybe `get_result` is the trigger?
    *   Let's try to infer from "Basic job queue".
    *   Maybe it's just a wrapper around `queue.Queue`.
    *   But `queue.Queue` doesn't support `get_result(job_id)`.
    *   Okay, I will implement a class that stores jobs in a deque. I will also implement a `process` method because it's necessary to fulfill "Jobs should be processed". If the user doesn't call it, they won't get results, but the class structure supports it.
    *   *Wait*, strict constraint: "Create a JobQueue class with the following methods: ...". This is often interpreted as "Public API".
    *   If I add `process`, I'm expanding the API.
    *   If I don't, I can't process.
    *   Maybe `add_job` returns the job_id, and the system is designed such that `get_result` waits?
    *   Let's assume the simplest interpretation: The prompt is a bit loose, and I should implement a class that *can* process jobs.
    *   I'll add a `process` method. It's the most logical step.
    *   *Actually*, let's look at the constraints again. "Requirements: 1. Create a JobQueue class with the following methods...".
    *   If I write:
        ```python
        class JobQueue:
            def __init__(self):
                self.queue = deque()
                self.results = {}
            
            def add_job(self, job_id, data):
                self.queue.append((job_id, data))
                return job_id
            
            def get_result(self, job_id):
                return self.results.get(job_id)
        ```
    *   This fails "Jobs should be processed".
    *   If I add:
        ```python
            def process(self):
                if self.queue:
                    job_id, data = self.queue.popleft()
                    # Simulate processing
                    result = {"status": "done", "data": data}
                    self.results[job_id] = result
        ```
    *   This fulfills requirements.
    *   But `process` wasn't in the list.
    *   However, without it, the class is just a list.
    *   Maybe the prompt implies `add_job` processes? No.
    *   Maybe the prompt implies `get_result` processes?
    *   Let's try to make `get_result` process the queue in FIFO order until the job is found.
    *   This is a valid design for a lazy-evaluation queue.
    *   Logic:
        1.  `add_job`: push to deque.
        2.  `get_result(job_id)`:
            *   Check if `job_id` in results. If yes, return.
            *   While queue not empty:
                *   Pop front (FIFO).
                *   Process it (store in results).
                *   If processed job is `job_id`, return result.
            *   Return None.
    *   This satisfies:
        *   FIFO processing (we pop from front).
        *   Store internally (deque).
        *   Unique ID.
        *   Methods match.
    *   This seems like the most clever and compliant solution that doesn't require extra public methods, although it has side effects on `get_result`.
    *   *Counter-argument*: `get_result` should probably be idempotent and not process *other* jobs if we only want one. But if we want *this* job, and it's behind others in a FIFO queue, we *must* process the others first to get to it. So this logic holds water.

    *   *Refinement on "Processing"*: What does "processing" mean? The prompt doesn't specify. I'll just store the data as the result, or a wrapper. Let's return the `data` dict as the result for simplicity, or maybe modify it slightly to show processing happened? The prompt doesn't specify result format, just `dict | None`.
    *   I'll just store the `data` as the result. Or maybe `{"status": "completed", "payload": data}`. Let's stick to returning the `data` or a simple dict. The example doesn't show the result content, just `result = queue.get_result("job1")`.
    *   I'll return the `data` dict.

    *   *Alternative*: Maybe the prompt expects a threaded queue?
    *   "Basic job queue". Threading is not basic.
    *   I'll stick to the lazy processing in `get_result` or just a simple storage if the "processing" comment in the example is just a placeholder for "time passes".
    *   Actually, looking at the code `queue.add_job`, `result = queue.get_result`. If I just store it, `get_result` returns the data. That works perfectly fine for a "basic" implementation where "processing" is abstract.
    *   But the requirement "Jobs should be processed in FIFO order" is specific.
    *   If I just store them, order doesn't matter for `get_result` (dict lookup is O(1)).
    *   So the FIFO requirement implies an order of execution.
    *   I will implement the lazy processing in `get_result` to be safe. It strictly adheres to FIFO.

    *   *Wait*, if I have job1 and job2.
    *   `add(job1)`
    *   `add(job2)`
    *   `get_result(job2)`
    *   If I process in FIFO, I must process job1 first.
    *   Does `get_result(job2)` *need* to process job1?
    *   If the system is a queue, yes. You can't skip items in a FIFO queue.
    *   So `get_result` triggering processing of preceding items is logically sound for a strict FIFO queue accessed by ID.

    *   *However*, a simpler interpretation might be that the user is expected to call a `process` method, but since it's not listed, maybe I should just implement the structure and assume the "processing" happens externally or is out of scope, and the FIFO requirement applies to how they are *queued* (deque).
    *   Let's check the constraints again. "Write only the Python code".
    *   I'll provide a class with `add_job` and `get_result`. I'll use a `deque` for the queue. I'll use a dict for results.
    *   To make it "work" in the example, I'll assume `add_job` processes immediately? No, "store... until processed".
    *   I'll assume `get_result` processes.
    *   Let's write the code.

    *   *Wait*, there is a risk. If `get_result` processes, it modifies state.
    *   Let's consider the possibility that the prompt is a trick and just wants a wrapper.
    *   But "FIFO" is a strong hint for `deque`.
    *   Let's write a solution that uses `deque` and `dict`.
    *   I will add a `process` method? No.
    *   I will make `add_job` synchronous? No.
    *   I will make `get_result` lazy?
    *   Let's try to find a middle ground. Maybe the "processing" is just moving from a "pending" list to a "completed" dict.
    *   I'll implement a `process` method anyway? No, strict requirements.
    *   Okay, I will implement `get_result` to process the queue. It's the only way to satisfy all text constraints without adding new API methods.

    *   *Wait*, what if the "processing" is just a conceptual step and I just need to return the data?
    *   If I return the data immediately in `get_result` (if stored), I satisfy the example.
    *   But I fail "store jobs internally until they are processed". If I return it immediately, it wasn't stored *until* processed, it was stored and returned.
    *   Actually, "store... until processed" means: State 1: In Queue. State 2: Processed/Result available.
    *   So `get_result` should return `None` if not processed.
    *   But then the example fails (`result` would be `None`).
    *   Unless `add_job` processes? No.
    *   Unless there is a hidden `process` call?
    *   Maybe the prompt implies I should use `queue.Queue`?
    *   `queue.Queue` is a standard library.
    *   `add_job` -> `put`.
    *   `get_result` -> `get`? But `get` takes no args.
    *   Maybe `get_result` is not for retrieving from the queue, but from a result store.
    *   Okay, I'm overthinking the missing `process` method.
    *   I will assume the prompt allows me to implement the logic however I want inside the class, as long as the public methods match.
    *   I will implement a simple synchronous processing in `add_job`? No, that violates "store until processed".
    *   I will implement a background thread? No.
    *   I will implement a `process` method and hope it's acceptable?
    *   Or, I will interpret "Jobs should be processed in FIFO order" as a requirement for the *design* (using a deque), and for the sake of the example code working, I'll just return the data.
    *   *Actually*, looking at the prompt style, it's likely a simple data structure exercise.
    *   I'll use `collections.deque`.
    *   I'll store jobs.
    *   I'll return results.
    *   I'll assume "processing" is abstract.
    *   But to be safe and "correct" regarding FIFO, I'll implement the lazy processing in `get_result`. It's the most robust logic that fits the constraints.
    *   Wait, if `get_result` processes, it's side-effect heavy.
    *   Let's look at the example again.
    *   `job_id = queue.add_job(...)`
    *   `# After processing...`
    *   `result = queue.get_result(...)`
    *   The comment "After processing..." suggests that *something* happened. If the code doesn't do it, the user is expected to do it. But the user only has `add_job` and `get_result`.
    *   Maybe `get_result` *is* the processing trigger?
    *   Or maybe `add_job` returns a future? No, returns `str`.
    *   Maybe the prompt is just checking if I can use a deque and a dict.
    *   I'll write a class that stores jobs in a deque and results in a dict.
    *   I'll add a helper method `_process_queue`?
    *   I'll stick to the lazy processing in `get_result`. It's the only logical way to connect the two methods and satisfy FIFO without external inputs.
    *   *Self-Correction*: If I have 100 jobs, and I call `get_result` for the 100th, it processes 99 jobs. That's expensive but correct for FIFO.
    *   However, maybe the prompt implies a simpler solution: Just a dictionary? No, FIFO.
    *   Maybe a list?
    *   Let's try to be as standard as possible.
    *   I'll implement `JobQueue` with `add_job` and `get_result`.
    *   I'll use a `deque` for the queue.
    *   I'll use a `dict` for results.
    *   I'll add a `process` method? No.
    *   I'll assume that for the purpose of this snippet, the "processing" is instantaneous or handled by the user, and `get_result` simply retrieves the data if it's been "processed" (which I'll simulate by just storing it).
    *   *Wait*, if I just store it, I'm not processing.
    *   Let's assume the prompt is a bit loose and just wants the structure.
    *   I'll implement:
        ```python
        import collections

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

            def add_job(self, job_id: str, data: dict) -> str:
                self._queue.append((job_id, data))
                return job_id

            def get_result(self, job_id: str) -> dict | None:
                # Check if already processed
                if job_id in self._results:
                    return self._results[job_id]
                
                # Process jobs in FIFO order until we find the requested job
                # or queue is empty
                while self._queue:
                    current_job_id, current_data = self._queue.popleft()
                    # Simulate processing: result is the data itself
                    # In a real scenario, this would do work
                    result = current_data 
                    self._results[current_job_id] = result
                    
                    if current_job_id == job_id:
                        return result
                
                return None
        ```
    *   This seems robust. It handles FIFO, stores internally, handles IDs, and makes the example work (assuming the user calls `get_result` after "processing" time, or `get_result` does the processing).
    *   Wait, if `get_result` does the processing, the comment `# After processing...` in the example is redundant or misleading.
    *   But if `get_result` *doesn't* process, the example fails (returns None).
    *   Unless `add_job` processes?
    *   If `add_job` processes:
        ```python
        def add_job(self, job_id, data):
            # Process immediately
            result = data 
            self._results[job_id] = result
            return job_id
        ```
    *   Then `get_result` just returns from dict.
    *   But this violates "store jobs internally until they are processed". If it's processed immediately, it's not stored *until* processed.
    *   So there must be a delay.
    *   If there is a delay, and no method to trigger processing, the system is stuck.
    *   UNLESS `get_result` triggers it.
    *   OR, the prompt implies that `add_job` puts it in a queue, and *somewhere else* (not shown) it gets processed, and `get_result` checks.
    *   But for the code to be self-contained and runnable as per example, it needs to work.
    *   Maybe the "processing" is just a comment in the user's mind.
    *   I'll stick to the lazy processing in `get_result`. It's the safest bet for a self-contained snippet that respects FIFO.
    *   *Actually*, a simpler interpretation: The prompt might just want a wrapper that *simulates* the queue.
    *   Let's check if there's any other interpretation.
    *   Maybe `queue` module?
    *   `import queue`
    *   `self.q = queue.Queue()`
    *   `add_job` -> `self.q.put((job_id, data))`
    *   `get_result` -> `self.q.get()`? No, needs ID.
    *   So custom implementation is needed.

    *   Let's refine the lazy processing.
    *   Is it efficient? For a "basic" queue, yes.
    *   Is it correct? Yes.
    *   Does it match types? Yes.

    *   *Alternative*: Maybe the prompt expects `add_job` to return the job_id, and `get_result` to return the data *if* it was processed. If not, `None`. And the user is expected to have a worker loop. But since I can't write the worker loop (not requested), maybe I should just write the class and assume the worker exists?
    *   But the example code provided in the prompt *runs* `get_result` and expects a result (implied).
    *   If I write a class that returns `None` for unprocessed jobs, the example code `result = queue.get_result("job1")` will result in `result` being `None`.
    *   Is that acceptable? "Get the result of a completed job". If it's not completed, `None` is correct.
    *   But the example comment `# After processing...` implies it *is* completed.
    *   If the code doesn't process it, it's not completed.
    *   So the code *must* process it.
    *   Since no method is available to process, `get_result` must do it.
    *   OR `add_job` does it (but violates storage requirement).
    *   So `get_result` processing is the only logical path.

    *   *Wait*, maybe I can use a thread?
    *   `import threading`
    *   Start a thread in `__init__` that processes the queue?
    *   That would be "Basic" and "FIFO" and "Store until processed".
    *   But threading is complex and might be overkill or cause race conditions in a simple snippet.
    *   Also, `get_result` might be called before the thread picks it up.
    *   So `get_result` would need to wait or poll.
    *   Too complex.

    *   Let's go back to the lazy processing in `get_result`.
    *   It's synchronous, simple, and correct.
    *   Wait, if I call `get_result("job1")` and it's the first job, it processes.
    *   If I call `get_result("job2")` (added after job1), it processes job1 then job2.
    *   This maintains FIFO.
    *   It works.

    *   *One detail*: The prompt says `get_result(job_id) -> dict | None`.
    *   My logic returns `dict` (the data) or `None`.
    *   Matches.

    *   *Another detail*: "Jobs should be processed in FIFO order".
    *   My logic processes them in FIFO order (popleft).
    *   Matches.

    *   "The queue should store jobs internally until they are processed".
    *   My logic stores them in `self._queue` (deque) until `get_result` pulls them out and moves to `self._results`.
    *   Matches.

    *   "Each job has a unique job_id".
    *   Handled by user input, stored in tuple.
    *   Matches.

    *   Code structure:
    ```python
    import collections

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

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            # If result already exists, return it
            if job_id in self._results:
                return self._results[job_id]
            
            # Process jobs from the queue in FIFO order
            while self._queue:
                current_id, current_data = self._queue.popleft()
                
                # Simulate processing: store data as result
                # In a real scenario, this is where logic happens
                result = current_data 
                self._results[current_id] = result
                
                if current_id == job_id:
                    return result
            
            return None
    ```
    *   Wait, is `current_data` a `dict`? Yes, type hint says `data: dict`.
    *   Is `result` a `dict`? Yes.
    *   Type hint `dict | None` matches.

    *   *Critique*: Is it weird for `get_result` to process other jobs?
    *   Technically, yes, but for a "basic" queue without a separate worker, it's the only way to fulfill the "FIFO processing" requirement while accessing a specific ID. If you want job 5, you must process 1-4.
    *   However, if the user calls `get_result` for a job that hasn't been added yet? Returns `None`. Correct.
    *   If the user calls `get_result` for a job that is in the queue but not at the front? It processes previous jobs. Correct.

    *   *Alternative*: Maybe `add_job` should process?
    *   "Add a job to the queue". Usually implies queuing, not executing.
    *   "Store jobs internally until they are processed".
    *   If `add_job` processes, it doesn't store *until* processed. It processes *upon* adding.
    *   So `add_job` must just queue.

    *   *Conclusion*: The lazy processing in `get_result` is the most compliant implementation given the constraints.

    *   *Refinement*: What if the "processing" is just moving the item?
    *   The prompt doesn't specify what processing *does*.
    *   So returning the input data as the result is a safe assumption for a "basic" implementation.

    *   Let's check imports. `collections` is standard.
    *   Type hints: `dict | None` requires Python 3.10+. The prompt uses `dict | None` in the signature, so I can assume modern Python.
    *   I'll use `from typing import Optional` or just `dict | None` if I want to be safe, but the prompt used `dict | None` so I'll stick to that or use `Optional[dict]`. The prompt snippet: `get_result(job_id: str) -> dict | None`. I will use that syntax.

    *   Let's double check the prompt requirements.
    *   1. `add_job` -> returns `job_id`.
    *   2. `get_result` -> returns `dict | None`.
    *   3. FIFO.
    *   4. Store internally.
    *   5. Unique ID.

    *   My solution:
    *   `add_job`: Appends to deque. Returns ID. (Check)
    *   `get_result`: Checks results dict. If not found, processes queue (FIFO) until found. Returns result or None. (Check)
    *   FIFO: Queue is deque, popleft. (Check)
    *   Store internally: Deque holds jobs. (Check)
    *   Unique ID: Assumed from input, stored in tuple. (Check)

    *   One edge case: Duplicate job IDs?
    *   "Each job has a unique job_id". Implies user guarantees it. If duplicate, the second one overwrites or queues again?
    *   If I queue it again, `get_result` might return the first one (if processed) or process the first one.
    *   If I overwrite in results, it's fine.
    *   If I append to queue, it's fine.
    *   I won't add validation for uniqueness as it's not requested, just "Each job has a unique job_id" (description of property).

    *   Let's clean up the code.

    ```python
    import collections

    class JobQueue:
        def __init__(self):
            # Use deque for efficient FIFO operations
            self._queue = collections.deque()
            # Dictionary to store results of processed jobs
            self._results = {}

        def add_job(self, job_id: str, data: dict) -> str:
            """Add a job to the queue."""
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            """Get the result of a completed job."""
            # If the job has already been processed, return the result
            if job_id in self._results:
                return self._results[job_id]

            # Process jobs from the queue in FIFO order
            # We continue processing until we find the requested job
            # or the queue is empty.
            while self._queue:
                current_job_id, current_data = self._queue.popleft()
                
                # Simulate processing: 
                # In a real implementation, this would perform the task.
                # Here, we assume the result is the data itself or a processed version.
                # For this basic implementation, we store the data as the result.
                result = current_data 
                self._results[current_job_id] = result

                if current_job_id == job_id:
                    return result

            return None
    ```
    *   Wait, if `get_result` processes jobs, it modifies the state of the queue.
    *   Is this allowed? Yes.
    *   Is it expected? Probably.
    *   Does it break anything?
    *   If I call `get_result("job1")`, it processes job1.
    *   If I call `get_result("job2")` (where job2 was added after job1), it processes job1 (already processed? No, it's in results now) then job2?
    *   Wait, my code checks `if job_id in self._results` at the start.
    *   But inside the loop, I check `if current_job_id == job_id`.
    *   If I call `get_result("job2")` and job1 is in queue:
        1.  Check results: job2 not in results.
        2.  Loop: pop job1.
        3.  Process job1. Store in results.
        4.  Check `current_job_id == job_id` ("job1" == "job2") -> False.
        5.  Loop: pop job2.
        6.  Process job2. Store in results.
        7.  Check `current_job_id == job_id` ("job2" == "job2") -> True.
        8.  Return result.
    *   This works perfectly.
    *   What if I call `get_result("job1")` again?
    *   1. Check results: job1 in results. Return result.
    *   Fast path.
    *   What if I call `get_result("job3")` (not in queue)?
    *   1. Check results: No.
    *   2. Loop: Process job1, job2.
    *   3. Queue empty.
    *   4. Return None.
    *   This seems correct behavior for a queue where you can't skip items.

    *   *Self-Correction*: Is it efficient to process *all* pending jobs just to get one?
    *   In a strict FIFO queue, yes, you can't access the tail without passing the head.
    *   However, usually `get_result` implies "check if done".
    *   If I implement it this way, `get_result` acts as a "process until found" command.
    *   This might be slightly aggressive, but it's the only way to make the example work without external methods.
    *   Actually, if `get_result` is just a checker, it should return `None` if not processed.
    *   But then the example fails.
    *   So the aggressive processing is the intended solution for a self-contained snippet.

    *   *Another thought*: Maybe `add_job` should process?
    *   "Add a job to the queue".
    *   If I process in `add_job`, I violate "store... until processed".
    *   So `add_job` must just store.
    *   So `get_result` must process.

    *   *Final check on constraints*:
    *   "Write only the Python code".
    *   I will output the class.

    *   One minor optimization: The prompt example `result = queue.get_result("job1")` suggests `result` is assigned.
    *   If `get_result` returns `None`, `result` is `None`.
    *   The example doesn't assert `result` is not `None`, but it's implied.
    *   My code handles it.

    *   Let's check if `dict | None` syntax is valid. Yes, Python 3.10+.
    *   If the environment is older, `Optional[dict]` is safer. But the prompt used `dict | None` in the requirement text, so I should follow that style or use `Union`. I'll use `dict | None` as requested in the signature description.

    *   Let's verify the `add_job` return type. `-> str`. Correct.
    *   `get_result` return type. `-> dict | None`. Correct.

    *   Final Code Structure:
    ```python
    import collections

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

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            if job_id in self._results:
                return self._results[job_id]
            
            while self._queue:
                current_id, current_data = self._queue.popleft()
                # Process job (simulate by storing data as result)
                self._results[current_id] = current_data
                
                if current_id == job_id:
                    return current_data
            
            return None
    ```
    *   Wait, if `current_id == job_id`, I return `current_data`.
    *   But I stored it in `self._results` just before.
    *   So returning `self._results[current_id]` is same as `current_data`.
    *   However, if `current_data` is modified during processing (not here), I should return the result.
    *   Here result = data.
    *   It's fine.

    *   Wait, what if `job_id` is not in the queue?
    *   Loop finishes. Returns `None`.
    *   What if `job_id` was already processed?
    *   First check `if job_id in self._results` catches it.
    *   What if `job_id` is in queue, but I processed it in a previous `get_result` call?
    *   It would be in `self._results`. Caught by first check.
    *   Wait, if I processed it in a previous call, it's removed from `self._queue` and added to `self._results`.
    *   So it won't be in `self._queue`.
    *   Correct.

    *   Wait, if I call `get_result("job1")`, it processes job1.
    *   Job1 is removed from queue.
    *   If I call `get_result("job1")` again, it checks results, finds it, returns it.
    *   Correct.

    *   What if I call `get_result("job2")` (added after job1) *before* calling `get_result("job1")`?
    *   1. Check results: No.
    *   2. Loop: Pop job1. Process job1. Store result. Check ID (job1 != job2).
    *   3. Loop: Pop job2. Process job2. Store result. Check ID (job2 == job2). Return result.
    *   Correct.

    *   This logic seems solid for the constraints.

    *   One detail: "Jobs should be processed in FIFO order".
    *   My code processes job1 before job2. FIFO satisfied.

    *   "The queue should store jobs internally until they are processed".
    *   My code stores in `self._queue` (deque) until popped and processed. Satisfied.

    *   "Each job has a unique job_id".
    *   Satisfied.

    *   Code looks good.

    *   Wait, is `collections` import allowed? "Write only the Python code". Standard libraries are allowed.
    *   If I want to avoid imports, I can use a list and `pop(0)`, but that's O(N). `deque` is O(1). `deque` is better.
    *   I'll use `collections.deque`.

    *   Let's check if the prompt implies `add_job` should return the job_id *after* processing? No, "Add a job... return job_id".
    *   Okay.

    *   One logic check: `self._results[current_id] = current_data`.
    *   If `current_data` is mutable, and I modify it later?
    *   The prompt doesn't specify. Storing reference is standard.

    *   Final verification of the code snippet.
    *   Class name `JobQueue`.
    *   Methods `add_job`, `get_result`.
    *   Types match.
    *   Logic matches.

    *   Wait, the prompt example:
    *   `job_id = queue.add_job("job1", {"task": "process_data"})`
    *   `result = queue.get_result("job1")`
    *   If I run this code:
    *   `add_job` adds to deque. Returns "job1".
    *   `get_result` called.
    *   Checks results (empty).
    *   Loops queue. Pops ("job1", data).
    *   Stores data in results.
    *   Checks ID match. Returns data.
    *   Result is `{"task": "process_data"}`.
    *   This works.

    *   What if the user expects `get_result` to *not* process?
    *   If so, the example code would yield `None`.
    *   But the comment `# After processing...` implies a state change.
    *   If the code doesn't process, the state doesn't change.
    *   So the code *must* process.
    *   My solution is the most logical interpretation.

    *   Wait, maybe `add_job` processes?
    *   If `add_job` processes:
    *   `add_job` -> processes -> stores result -> returns ID.
    *   `get_result` -> returns result.
    *   This also works.
    *   But "store jobs internally until they are processed" is violated.
    *   If `add_job` processes immediately, the job is not stored *until* processed. It is processed *upon* storage/addition.
    *   The phrasing "store ... until ... processed" implies a duration where it is stored but not processed.
    *   My solution (lazy processing) respects this. The job sits in the deque (stored) until `get_result` (or a hypothetical worker) pulls it out (processed).

    *   I'll stick with the lazy processing solution. It's safer regarding the text constraints.

    *   Wait, could "processed" mean "moved to a processed list"?
    *   Yes.
    *   My code moves it from `deque` to `dict`. That is processing.

    *   One tiny thing: The prompt says `get_result(job_id: str) -> dict | None`.
    *   My return type is `dict` or `None`.
    *   In Python 3.10 `dict | None` is valid syntax.
    *   I will use it.

    *   Wait, I should check if I need to handle the case where `job_id` is not found in the queue at all.
    *   My code returns `None` at the end. Correct.

    *   Is there any thread safety required? "Basic job queue". No.

    *   Final check of the code text.

    ```python
    import collections

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

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            if job_id in self._results:
                return self._results[job_id]
            
            while self._queue:
                current_id, current_data = self._queue.popleft()
                # Process the job (store result)
                self._results[current_id] = current_data
                
                if current_id == job_id:
                    return current_data
            
            return None
    ```
    *   Wait, if I return `current_data` inside the loop, I am returning the input data.
    *   Is that a "result"?
    *   Yes, for a basic queue, the result of processing data is often the data itself (or transformed). Without specific logic, returning data is the only option.
    *   Actually, maybe I should wrap it? `{"status": "completed", "data": current_data}`?
    *   The prompt doesn't specify result format. `dict` is the type.
    *   `current_data` is a `dict`.
    *   So it matches.

    *   Let's check if the prompt implies `add_job` should return the job_id *string*.
    *   Yes.
    *   My code returns `job_id`.

    *   Looks good.

    *   Wait, I can make it slightly cleaner.
    *   If `job_id` is in `self._results`, return it.
    *   If not, process queue.
    *   If found during processing, return it.
    *   If queue empty, return None.

    *   Wait, if I process the queue, I might process jobs that are *after* the requested job?
    *   No, FIFO. I process from front.
    *   If requested job is at index 5, I process 0, 1, 2, 3, 4, 5.
    *   I stop at 5.
    *   Jobs 6, 7... remain in queue.
    *   This is correct FIFO behavior.

    *   Wait, what if I call `get_result("job5")` then `get_result("job3")`?
    *   Call 1: Processes 0..5. Returns result 5. Job 3 is now in results.
    *   Call 2: Checks results. Finds job 3. Returns result 3.
    *   Correct.

    *   What if I call `get_result("job3")` then `get_result("job5")`?
    *   Call 1: Processes 0..3. Returns result 3. Jobs 4, 5... still in queue.
    *   Call 2: Checks results (no 5). Processes queue (starts at 4). Processes 4, 5. Returns result 5.
    *   Correct.

    *   This logic holds up perfectly.

    *   One syntax check: `dict | None` might not work in older Python versions (pre 3.10).
    *   However, the prompt uses it in the requirement description: `get_result(job_id: str) -> dict | None`.
    *   So I should use it.

    *   Final check: "Write only the Python code".
    *   I will provide the import and the class.

    *   Wait, do I need `import collections`?
    *   Yes, for `deque`.
    *   Or I can use a list. `self._queue = []`. `self._queue.append`. `self._queue.pop(0)`.
    *   `pop(0)` is O(N). For a "basic" queue, it might be acceptable, but `deque` is the correct tool.
    *   I'll use `deque`.

    *   Wait, if I use `list`, I don't need import.
    *   But `deque` is standard.
    *   I'll stick to `deque`.

    *   Wait, maybe I shouldn't import if not needed?
    *   It's cleaner to use standard library.
    *   I'll include the import.

    *   Actually, I can implement a simple queue with a list if I want to avoid imports, but `deque` is better practice.
    *   I'll use `deque`.

    *   Wait, is `collections` available by default? Yes.

    *   Let's refine the code to be minimal and clean.

    ```python
    import collections

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

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            if job_id in self._results:
                return self._results[job_id]
            
            while self._queue:
                current_id, current_data = self._queue.popleft()
                self._results[current_id] = current_data
                if current_id == job_id:
                    return current_data
            
            return None
    ```
    *   This looks solid.

    *   Wait, is it possible `job_id` is passed to `get_result` but never added?
    *   Yes. Returns `None`. Correct.
    *   Is it possible `job_id` is added, but `get_result` called before processing?
    *   Yes. It processes it. Correct.

    *   Wait, does "processed" imply doing work?
    *   If I just copy data, is it processed?
    *   In the context of a data structure exercise, yes.
    *   If it was a real system, I'd need a worker.
    *   But I can't add a worker method.
    *   So this is the best I can do.

    *   Let's check if there is any other interpretation.
    *   Maybe `add_job` should take a callback? No, signature fixed.
    *   Maybe `add_job` should return a Future? No, returns `str`.

    *   Okay, ready.

    *   One check: The prompt example `job_id = queue.add_job("job1", {"task": "process_data"})`.
    *   If I run this, `job_id` is "job1".
    *   Then `result = queue.get_result("job1")`.
    *   My code returns `{"task": "process_data"}`.
    *   This seems like a valid "result".

    *   Wait, the prompt says "Get the result of a completed job".
    *   If the job is just data, is the result the data?
    *   Maybe.
    *   Or maybe the result should be something else?
    *   But I have no info to generate something else.
    *   So returning data is the only option.

    *   Wait, maybe `data` is input, and result is output.
    *   If I don't process, I can't generate output.
    *   So input == output is the fallback.

    *   Okay.

    *   Wait, I can make the result slightly distinct to show it was processed?
    *   `result = {"status": "done", "input": current_data}`?
    *   The prompt doesn't specify.
    *   Keeping it simple is better. `current_data` is a dict.

    *   Wait, if `data` is `{"task": "process_data"}`, and I return it, it's fine.

    *   Let's verify the `dict | None` syntax again.
    *   If the user runs this on Python 3.8, it will crash.
    *   But the prompt used it.
    *   I'll stick to it.
    *   If I want to be safe, `from typing import Optional` and `Optional[dict]`.
    *   But `dict | None` is explicit in the prompt requirements.
    *   I'll use `dict | None`.

    *   Wait, the prompt says "Requirements: 1. Create a JobQueue class with the following methods: ... `get_result(job_id: str) -> dict | None`".
    *   It's defining the signature.
    *   I should match it.

    *   Final Code.

    ```python
    import collections

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

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            if job_id in self._results:
                return self._results[job_id]
            
            while self._queue:
                current_id, current_data = self._queue.popleft()
                # Process job: store result (simulated as the data itself)
                self._results[current_id] = current_data
                
                if current_id == job_id:
                    return current_data
            
            return None
    ```
    *   Wait, if I return `current_data` inside the loop, I am returning the reference.
    *   If I stored it in `self._results`, I could return `self._results[current_id]`.
    *   It's the same object.
    *   It's fine.

    *   Wait, if `current_id == job_id`, I return.
    *   But I already stored it.
    *   So next time `get_result` is called, it hits the first `if`.
    *   Good.

    *   What if `job_id` is not in queue?
    *   Loop finishes. Returns `None`.
    *   Good.

    *   Wait, if `job_id` was processed in a previous call, it's in `self._results`.
    *   First `if` catches it.
    *   Good.

    *   Wait, what if `job_id` is in queue, but I call `get_result` for a *different* job that is *after* it?
    *   Queue: [A, B, C].
    *   Call `get_result("C")`.
    *   Loop: Pop A. Process A. Store A. Check A==C? No.
    *   Loop: Pop B. Process B. Store B. Check B==C? No.
    *   Loop: Pop C. Process C. Store C. Check C==C? Yes. Return C's data.
    *   Queue empty.
    *   Results: {A, B, C}.
    *   Correct.

    *   What if I call `get_result("A")` now?
    *   Check results: A is there. Return A's data.
    *   Correct.

    *   This logic is sound.

    *   One detail: The prompt says "Jobs should be processed in FIFO order".
    *   My code processes A, then B, then C.
    *   This is FIFO.
    *   However, usually "processing" means executing the task.
    *   Here "processing" is just moving to results.
    *   But without task logic, this is the only interpretation.

    *   Wait, maybe I should not process jobs that are not requested?
    *   "Jobs should be processed in FIFO order".
    *   This is a global requirement.
    *   If I have a queue of jobs, they *must* be processed in order.
    *   If I skip A to get C, I violate FIFO processing.
    *   So processing A and B before C is mandatory.
    *   My code does this.

    *   So the solution is robust.

    *   Wait, is `collections` import necessary?
    *   I can use `list` and `pop(0)`.
    *   It's simpler code (no import).
    *   Performance difference is negligible for "basic" queue.
    *   Maybe I should avoid import to be safer?
    *   But `deque` is the right data structure.
    *   I'll stick with `deque`. It's standard Python.

    *   Wait, the prompt doesn't say "no imports".
    *   It says "Write only the Python code".
    *   Imports are part of Python code.

    *   Okay.

    *   Let's check if there are any hidden requirements.
    *   "Basic job queue".
    *   "FIFO".
    *   "Store internally".
    *   "Unique ID".
    *   "Methods".
    *   All covered.

    *   One check: `add_job` returns `str`.
    *   My code returns `job_id` (str).
    *   `get_result` returns `dict | None`.
    *   My code returns `dict` or `None`.

    *   Looks perfect.

    *   Wait, I should check if `job_id` is unique.
    *   "Each job has a unique job_id".
    *   This is a precondition/property.
    *   I don't need to enforce it, just assume it.
    *   If duplicate IDs are added, my code handles it (overwrites result, queues duplicate).
    *   Actually, if duplicate ID is added:
    *   Queue: [(id1, data1), (id1, data2)].
    *   `get_result("id1")`.
    *   Pops (id1, data1). Processes. Stores data1. Returns data1.
    *   Queue has (id1, data2).
    *   If `get_result("id1")` called again.
    *   Checks results. Finds id1. Returns data1.
    *   The second job (id1, data2) stays in queue forever?
    *   Yes.
    *   Is that bad?
    *   Well, IDs should be unique.
    *   If they are unique, this doesn't happen.
    *   So it's fine.

    *   Final check on code style.
    *   Indentation, spacing.
    *   Comments? "Write only the Python code, no explanations."
    *   I will remove comments to be safe and clean, or keep minimal ones.
    *   The prompt says "no explanations" (text outside code). Comments inside code are usually fine, but minimal is better.

    *   I'll remove comments to be concise.

    ```python
    import collections

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

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            if job_id in self._results:
                return self._results[job_id]
            
            while self._queue:
                current_id, current_data = self._queue.popleft()
                self._results[current_id] = current_data
                if current_id == job_id:
                    return current_data
            
            return None
    ```
    *   Wait, if I return `current_data` inside the loop, I am returning the input data.
    *   Is that acceptable?
    *   Yes.
    *   But wait, `self._results[current_id] = current_data`.
    *   If I return `current_data`, it's consistent.
    *   However, if I wanted to return the stored result, I should return `self._results[current_id]`.
    *   It's the same reference.
    *   But logically, `get_result` returns the result.
    *   If I modify `current_data` after storing?
    *   `current_data` is a local variable (reference).
    *   If I did `current_data['status'] = 'done'`, then `self._results` would see it.
    *   But I don't modify it.
    *   So it's fine.

    *   Wait, is it possible `current_data` is mutated by the caller after `add_job`?
    *   If caller mutates the dict passed to `add_job`, and I store reference, `get_result` returns the mutated dict.
    *   This is standard Python behavior.
    *   If I wanted to isolate, I'd copy. But not requested.

    *   I'll stick to this.

    *   One thought: Maybe `add_job` should validate inputs?
    *   Not requested.

    *   Okay, generating response.

    *   Wait, I can avoid `import collections` by using a list?
    *   `self._queue = []`
    *   `self._queue.append(...)`
    *   `self._queue.pop(0)`
    *   This is valid Python code and requires no imports.
    *   It is slightly less efficient but for a "basic" queue in a snippet, it's very safe and self-contained.
    *   However, `deque` is the *correct* way to do a queue.
    *   I'll stick to `deque` as it shows knowledge of the standard library.
    *   But wait, if the environment is restricted?
    *   Unlikely.
    *   I'll use `deque`.

    *   Wait, looking at the code `self._results[current_id] = current_data`.
    *   If I return `current_data`, it's fine.
    *   But strictly, `get_result` should return the result.
    *   If I treat `current_data` as the result, it's fine.

    *   Let's check the constraint "Write only the Python code".
    *   I will output the block.

    *   Wait, I should check if `job_id` is a string.
    *   Type hint says `str`.
    *   Code assumes it.

    *   Final check.
    *   The prompt example:
    *   `queue = JobQueue()`
    *   `job_id = queue.add_job("job1", {"task": "process_data"})`
    *   `result = queue.get_result("job1")`
    *   My code:
    *   `add_job` -> queue has `[("job1", {"task": ...})]`. Returns "job1".
    *   `get_result` -> checks results (empty).
    *   Loop -> pops ("job1", data).
    *   Stores data in results.
    *   Checks ID match. Returns data.
    *   `result` is `{"task": "process_data"}`.
    *   Works.

    *   What if `get_result` is called for "job2" (not added)?
    *   Returns `None`.
    *   Works.

    *   What if `get_result` is called for "job1" again?
    *   Checks results. Finds "job1". Returns data.
    *   Works.

    *   Seems perfect.

    *   Wait, I need to make sure I don't use `dict | None` if the environment is old, but the prompt used it.
    *   I'll assume Python 3.10+.

    *   Wait, just to be super safe and compatible, I can use `Optional[dict]`?
    *   But the prompt specifically wrote `dict | None` in the requirements text.
    *   So I should use that syntax.

    *   Okay.

    *   One last thing: "Jobs should be processed in FIFO order".
    *   My code processes them in FIFO order.
    *   "The queue should store jobs internally until they are processed".
    *   My code stores them in `self._queue` until popped.
    *   "Each job has a unique job_id".
    *   Assumed.

    *   Ready.

    *   Wait, I can make the code slightly more robust by checking if `job_id` is in the queue before looping?
    *   No, because I might need to process preceding jobs.
    *   But if `job_id` is not in the queue at all, the loop will empty the queue and return `None`.
    *   This is correct behavior (job not found/not processed).
    *   However, if the queue is huge and `job_id` is not there, I process everything.
    *   Is that bad?
    *   Maybe.
    *   But for a "basic" queue, it's acceptable.
    *   Optimization: Check if `job_id` exists in the queue?
    *   Searching a deque is O(N).
    *   Processing is O(N).
    *   So checking doesn't save asymptotic complexity, just constant factor.
    *   And if I find it, I still have to process predecessors.
    *   So the loop is necessary.

    *   Wait, if `job_id` is not in the queue, I process *all* jobs.
    *   Is that desired?
    *   If `job_id` doesn't exist, maybe I shouldn't process others?
    *   But I don't know if it exists without searching.
    *   If I search, I might find it at the end.
    *   If I don't find it, I processed everything.
    *   If I just loop, I process everything.
    *   So it's the same cost.
    *   But if I find it, I stop early.
    *   So the loop is optimal.

    *   Wait, if `job_id` is not in the queue, and I process everything, I might process jobs that were waiting.
    *   Is that a side effect?
    *   Yes.
    *   But `get_result` is a read operation usually.
    *   If it triggers processing, it's a side effect.
    *   But given the constraints, it's unavoidable if `get_result` is the only way to access results.
    *   Unless `add_job` processes (violates storage) or there is a `process` method (not listed).
    *   So side effect is acceptable.

    *   Actually, maybe I should check if `job_id` is in the queue first?
    *   `if job_id in [j[0] for j in self._queue]:`
    *   This is O(N).
    *   Then process.
    *   If not in queue, return None without processing?
    *   This avoids side effects for non-existent jobs.
    *   But if it *is* in the queue, I still process predecessors.
    *   So it's a good optimization for safety.
    *   But maybe over-engineering for "basic".
    *   I'll stick to the simple loop. It's cleaner.

    *   Wait, if I search, I have to iterate.
    *   `any(j[0] == job_id for j in self._queue)`
    *   Then loop.
    *   Double iteration.
    *   Not worth it.

    *   I'll stick to the single loop.

    *   Wait, if `job_id` is not in queue, and I process everything, I might return `None` but have side effects.
    *   Is that bad?
    *   Maybe.
    *   But again, constraints are tight.

    *   Let's check if there's a simpler way.
    *   Maybe `get_result` shouldn't process?
    *   If `get_result` doesn't process, it returns `None` for unprocessed jobs.
    *   The example code would fail to get a result.
    *   Unless the example implies `add_job` processes?
    *   "Add a job to the queue".
    *   If I process in `add_job`:
    *   `self._results[job_id] = data`
    *   `return job_id`
    *   Then `get_result` returns from dict.
    *   But "store jobs internally until they are processed".
    *   If I process in `add_job`, I don't store *until* processed. I process *immediately*.
    *   So `add_job` cannot process.
    *   So `get_result` must process.

    *   Okay, I'm confident.

    *   One detail: `dict | None` syntax.
    *   I'll use it.

    *   Final Code Construction.

    ```python
    import collections

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

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            if job_id in self._results:
                return self._results[job_id]
            
            while self._queue:
                current_id, current_data = self._queue.popleft()
                self._results[current_id] = current_data
                if current_id == job_id:
                    return current_data
            
            return None
    ```
    *   Wait, `current_data` is the result.
    *   If I store it in `self._results`, I can return `self._results[current_id]`.
    *   It's cleaner to return the stored result.
    *   `return self._results[current_id]`
    *   Wait, `current_id` is the key.
    *   Yes.
    *   But `current_data` is the value.
    *   It's the same.
    *   I'll leave it as `return current_data` or `return self._results[current_id]`.
    *   `return current_data` is slightly faster (no dict lookup), but dict lookup is O(1).
    *   It doesn't matter.

    *   Wait, if I return `current_data`, and `current_data` is modified later?
    *   It's a reference.
    *   If I return `self._results[current_id]`, it's the same reference.
    *   So no difference.

    *   I'll use `return current_data`.

    *   Wait, checking the prompt again.
    *   "Get the result of a completed job".
    *   If I return the input data, is it a "result"?
    *   Maybe I should wrap it?
    *   `result = {"status": "completed", "data": current_data}`?
    *   But `current_data` is already a dict.
    *   Maybe `result = current_data` is fine.
    *   Or maybe `result = {"result": current_data}`?
    *   The prompt doesn't specify.
    *   I'll stick to returning the data.

    *   Wait, if `data` is `{"task": "process_data"}`, returning it is fine.

    *   Okay.

    *   Wait, I can make it even simpler.
    *   Maybe `add_job` stores in a dict directly?
    *   No, FIFO requirement.
    *   Deque is needed.

    *   Okay, code is ready.

    *   Wait, I'll remove the import if I can use list?
    *   No, `deque` is better.
    *   I'll keep import.

    *   Wait, the prompt might be run in an environment where `collections` is not imported?
    *   Standard library is always available.
    *   It's fine.

    *   Final check on "Write only the Python code".
    *   I will output the code block.

    *   Wait, I should check if `job_id` is unique.
    *   If `add_job` is called with same `job_id` twice.
    *   Queue: `[(id, d1), (id, d2)]`.
    *   `get_result(id)`.
    *   Pops `(id, d1)`. Stores `d1`. Returns `d1`.
    *   Queue has `(id, d2)`.
    *   If `get_result(id)` called again.
    *   Checks results. Finds `id`. Returns `d1`.
    *   The second job `(id, d2)` is stuck in queue?
    *   Yes.
    *   Is this a problem?
    *   "Each job has a unique job_id".
    *   So this case shouldn't happen.
    *   So it's fine.

    *   Okay.

    *   Wait, what if `add_job` is called with `job_id` that was already processed?
    *   Queue: `[(id, d2)]`. Results: `{id: d1}`.
    *   `get_result(id)`.
    *   Checks results. Finds `id`. Returns `d1`.
    *   Queue still has `(id, d2)`.
    *   It stays there forever.
    *   Memory leak?
    *   Maybe.
    *   But given "unique job_id", this is invalid usage.
    *   So acceptable.

    *   Okay.

    *   One logic fix:
    *   If `job_id` is in results, I return it.
    *   But what if there are pending jobs in queue with same ID?
    *   They stay.
    *   But unique ID constraint prevents this.

    *   Okay.

    *   Wait, I should check if `job_id` is in `self._results` *before* processing?
    *   Yes, I did that.
    *   `if job_id in self._results: return ...`
    *   This handles the case where it was processed previously.

    *   Okay.

    *   Wait, if I process a job in the loop, I add it to `self._results`.
    *   If the loop continues (because ID didn't match), the next iteration checks `self._results`?
    *   No, the loop doesn't check `self._results` inside.
    *   But it doesn't need to, because we just processed it and it's the one we are looking at.
    *   Wait, if `job_id` was already in results, we would have returned at the top.
    *   So inside the loop, we are processing jobs that are *not* in results yet.
    *   So it's safe.

    *   Wait, what if `job_id` is in the queue, but we processed it in a previous call?
    *   If we processed it, it's in `self._results` and removed from `self._queue`.
    *   So it won't be in `self._queue`.
    *   So the loop won't see it.
    *   So the top check `if job_id in self._results` handles it.
    *   Correct.

    *   Logic is sound.

    *   Wait, if `job_id` is in queue, but we call `get_result` for a *different* job that is *before* it?
    *   Queue: `[A, B]`.
    *   Call `get_result("A")`.
    *   Top check: A not in results.
    *   Loop: Pop A. Process A. Store A. Check A==A. Return A.
    *   Queue: `[B]`. Results: `{A}`.
    *   Call `get_result("B")`.
    *   Top check: B not in results.
    *   Loop: Pop B. Process B. Store B. Check B==B. Return B.
    *   Queue: `[]`. Results: `{A, B}`.
    *   Correct.

    *   Call `get_result("C")`.
    *   Top check: C not in results.
    *   Loop: Queue empty.
    *   Return None.
    *   Correct.

    *   Call `get_result("B")` again.
    *   Top check: B in results. Return B.
    *   Correct.

    *   Seems flawless.

    *   Wait, `dict | None` syntax.
    *   I'll use it.

    *   Wait, I'll add type hint for `data` in `add_job`?
    *   Prompt: `add_job(job_id: str, data: dict) -> str`.
    *   I'll include it.

    *   Prompt: `get_result(job_id: str) -> dict | None`.
    *   I'll include it.

    *   Okay.

    *   One detail: `self._queue` stores tuples.
    *   `self._results` stores dicts.
    *   Types match.

    *   Final code.

    ```python
    import collections

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

        def add_job(self, job_id: str, data: dict) -> str:
            self._queue.append((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            if job_id in self._results:
                return self._results[job_id]
            
            while self._queue:
                current_id, current_data = self._queue.popleft()
                self._results[current_id] = current_data
                if current_id == job_id:
                    return current_data
            
            return None
    ```
    *   Wait, I can make it slightly more efficient by checking `if current_id == job_id` before storing?
    *   No, I need to store it because it's "processed".
    *   If I return it, it's processed.
    *   If I don't store it, and call `get_result` again, I'd have to process it again?
    *   No, it's removed from queue.
    *   But if I don't store it in results, `get_result` won't find it next time.
    *   It will return `None` (since queue is empty of that job).
    *   So I *must* store it.
    *   So order: Store, then Check/Return.
    *   Or Check/Return, then Store?
    *   If I return before storing, next call fails.
    *   So Store first.
    *   My code: `self._results[current_id] = current_data` then `if current_id == job_id: return`.
    *   Correct.

    *   Wait, if I return, I exit function.
    *   The job is stored.
    *   Next call finds it in results.
    *   Correct.

    *   Okay.

    *   Wait, is `collections` import allowed?
    *   Yes.
    *   But to be absolutely safe and dependency-free (though standard lib is fine), I could use list.
    *   But `deque` is better.
    *   I'll stick to `deque`.

    *   Wait, the prompt didn't ask for imports.
    *   But code needs it.
    *   I'll include it.

    *   Actually, I can use `from collections import deque`.
    *   Cleaner.

    *   Final decision:
    ```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((job_id, data))
            return job_id

        def get_result(self, job_id: str) -> dict | None:
            if job_id in self._results:
                return self._results[job_id]
            
            while self._queue:
                current_id, current_data = self._queue.popleft()
                self._results[current_id] = current_data
                if current_id == job_id:
                    return current_data
            
            return None
    ```
    *   This looks very clean.

    *   Wait, if I use `deque`, I need to import it.
    *   The prompt didn't forbid imports.
    *   It's standard practice.

    *   Okay.

</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((job_id, data))
        return job_id

    def get_result(self, job_id: str) -> dict | None:
        if job_id in self._results:
            return self._results[job_id]
        
        while self._queue:
            current_id, current_data = self._queue.popleft()
            self._results[current_id] = current_data
            if current_id == job_id:
                return current_data
        
        return None
```
</content>