<reasoning_content>
*   Goal: Implement a basic job queue in Python.
    *   Class: `JobQueue`.
    *   Methods:
        *   `add_job(job_id: str, data: dict) -> str`: Add a job, return `job_id`.
        *   `get_result(job_id: str) -> dict | None`: Get the result of a completed job.
    *   Logic:
        *   FIFO processing.
        *   Store jobs internally.
        *   Unique `job_id`.
    *   Note on "processing": The prompt asks for the *structure* of a queue. It doesn't explicitly ask for a worker thread or an asynchronous processor, but `get_result` implies that jobs eventually *get* results. However, if I just implement a storage mechanism, `get_result` will always return `None` unless I simulate processing. Looking at the requirement "Jobs should be processed in FIFO order", this implies there should be a mechanism to trigger processing or a worker.
    *   Wait, the prompt is slightly ambiguous: Does the `JobQueue` *handle* the processing or just the storage?
    *   Requirements check:
        1. `add_job` and `get_result`.
        2. FIFO order.
        3. Internal storage.
        4. Unique ID.
    *   If I implement just a storage queue, how does it get "processed"?
    *   Standard pattern for a "basic job queue" in an interview/coding task context:
        *   A way to add jobs.
        *   A way to process jobs (often a `process_next` method or a worker thread).
        *   A way to get results.
    *   Let's look at the example usage: `job_id = queue.add_job(...)` ... `# After processing...` ... `result = queue.get_result(...)`.
    *   The example suggests the processing happens "somewhere else" or via an internal mechanism.
    *   If I implement a `process_jobs` method or a background thread, it satisfies the FIFO requirement.
    *   Let's implement a version with:
        *   `collections.deque` for the queue (FIFO).
        *   A dictionary to store results.
        *   A dictionary to store pending jobs.
        *   A method to actually "process" the job (perhaps `process_next`) to make the code functional for a user to test.
    *   Actually, the prompt doesn't explicitly ask for a `process` method, but without it, `get_result` is useless. However, I must stick to the *exact* interface requested in Requirement 1.
    *   Interface requested:
        *   `add_job(job_id, data)`
        *   `get_result(job_id)`
    *   If I can only have those two methods, how is it processed?
        *   Maybe the `add_job` is expected to return immediately, and a separate thread handles it?
        *   Or maybe I should add a `process_jobs()` method just to make the logic demonstrable, but the prompt says "Create a JobQueue class with the following methods...", which might imply *only* those methods.
    *   Let's look at the requirement "Jobs should be processed in FIFO order". This strongly implies a worker or a processing step.
    *   I will include a simple `process_next` method or a way to simulate work so the code is actually "implementing a job queue" rather than just a dictionary.
    *   Actually, to be safest and cleanest:
        1. Use `collections.deque` for the FIFO queue.
        2. Use a `dict` for `results`.
        3. I'll add a `process_jobs` method or a `process_next_job` method to satisfy the "processed" logic, even if not explicitly listed in the required methods, because otherwise, the queue does nothing.
        4. *Wait*, re-reading: "Create a JobQueue class with the following methods...". If I add more, I'm technically going outside the spec, but if I don't, the code is incomplete.
        5. Let's try to implement it such that `add_job` puts it in a queue, and I'll provide a `process_next_job` method to allow the user to actually see the FIFO and result logic working.

    *   Refined Plan:
        - `self.queue`: `collections.deque` of `(job_id, data)`.
        - `self.results`: `dict` of `job_id: result`.
        - `add_job`: Append to deque.
        - `get_result`: Lookup in results.
        - To satisfy "Jobs should be processed in FIFO order", I will implement a `process_next_job` method.

    *   Wait, another interpretation: The prompt might want a background thread. But "basic" usually implies simplicity.
    *   Let's look at the `get_result` signature: `dict | None`. If a job isn't done, it returns `None`.
    *   I'll implement `process_next_job` to make it a complete, working example of a queue.

    *   Implementation details:
        - `collections.deque` for the queue.
        - `dict` for results.
        - `add_job`: push `(job_id, data)` to deque.
        - `process_next_job`: popleft from deque, perform some logic (e.g., return data or transform it), and save to `results`.
        - `get_result`: return from `results`.

    *   Let's check the requirement: "Jobs should be processed in FIFO order". If I don't have a method to process, I haven't implemented the requirement.

    ```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 process_next_job(self):
            if not self._queue:
                return None
            job_id, data = self._queue.popleft()
            # Simulating some processing
            result = {"status": "completed", "original_data": data}
            self._results[job_id] = result
            return result

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

    *   Wait, is there a way to satisfy the "processed" requirement without adding a method?
        *   If `add_job` triggers a thread? That's "basic" but slightly complex for a simple prompt.
        *   What if `get_result` checks if there is anything to process? No, that's not how queues work.
    *   I'll stick to the requested interface and add `process_next_job` as a way to demonstrate the FIFO and processing.

    *   Final check on requirements:
        - `add_job(job_id: str, data: dict) -> str` (Matches)
        - `get_result(job_id: str) -> dict | None` (Matches)
        - FIFO (Yes, via deque)
        - Store internally (Yes)
        - Unique job_id (User provides it, I return it)

    *   One detail: "Each job has a unique job_id that is returned when added". If the user provides the ID, it's their responsibility, but I should probably check for collisions or just assume the user follows the example. The example shows `job_id = queue.add_job("job1", ...)`.

    *   Wait, if I want to be extremely strictly compliant with "Create a JobQueue class with the following methods" (implying *only* these), and still satisfy the "processed in FIFO order", I could potentially process jobs inside `get_result`? No, that's bad design. I will include `process_next_job` to make the class functional.

    *   Actually, I'll implement it such that I can also provide a version that uses a simple internal processing loop if possible, but the simplest and most direct way is to include a processing method.

    *   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:
            self._queue.append((job_id, data))
            return job_id

        def process_next_job(self) -> dict | None:
            """Processes the next job in the queue (FIFO)."""
            if not self._queue:
                return None
            
            job_id, data = self._queue.popleft()
            # Simulate processing logic
            result = {"processed": True, "data": data}
            self._results[job_id] = result
            return result

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

    *   Wait, the prompt says "Write only the Python code, no explanations."

    *   Final Code Structure:
        - Import `deque`.
        - Class `JobQueue`.
        - `__init__`.
        - `add_job`.
        - `process_next_job` (to make the queue actually do something).
        - `get_result`.

    *   Actually, looking at many similar coding challenges, often the "processing" is implied to be something the user calls, or the class has a `run` or `work` method. I'll provide `process_next_job`.

    *   Let's double check the `add_job` return type: `-> str`. (Matches)
    *   `get_result` return type: `-> dict | None`. (Matches)

    *   One edge case: If `add_job` is called with a `job_id` that already exists, should it overwrite? The requirement says "Each job has a unique job_id". I'll assume IDs are unique as per requirements.

    *   Self-Correction: If I'm strictly following "Create a JobQueue class with the following methods", adding `process_next_job` might be seen as an extra. But without it, the "Jobs should be processed in FIFO order" requirement is impossible to implement in a static code snippet. I will add it.

    *   Wait, if I implement a background thread in `__init__`, it would satisfy the requirements *exactly* as written without adding extra methods to the public API.

    *   Background thread implementation:
        - `__init__`: starts a daemon thread.
        - `_worker`: while true, pop from deque, sleep/process, put in results.
        - `add_job`: add to deque.
        - `get_result`: check results.

    *   Is that "basic"? It's a bit more "real-world".
    *   Let's try the thread approach. It's much cleaner for the user's perspective (matching the example usage's comment `# After processing...`).

    *   Thread-based Implementation:
        1. `collections.deque` (for queue)
        2. `threading.Thread` (for worker)
        3. `threading.Lock` (to protect shared resources like `results` and `queue`)
        4. `time.sleep` (to simulate work)

    *   Wait, the prompt asks for a *basic* job queue. A background thread might be overkill. Let's look at the example again.
    *   `# After processing...`
    *   This comment implies that processing happens *between* the two calls. This could be manual or automatic.
    *   If I use a manual `process_next_job`, the user's code would look like:
        `queue.add_job(...)`
        `queue.process_next_job()`
        `queue.get_result(...)`
    *   If I use a thread, the user's code looks like:
        `queue.add_job(...)`
        `# wait a bit`
        `queue.get_result(...)`
    *   Given the "Write only code" instruction, I'll provide the most robust but simple version. I'll go with the manual processing method because it's easier to reason about in a simple code snippet and doesn't require handling thread lifecycle/shutdown in a "basic" implementation. However, I'll rename it to something like `run_next` or just `process_job`.

    *   Actually, I will provide a version where the processing is manual via `process_next_job` because it's the most standard way to implement a data structure in an interview context unless concurrency is specifically requested.

    *   Actually, I'll check if I can just implement the requirements and nothing else.
    *   If I only implement `add_job` and `get_result`, and I don't have a way to process, I haven't fulfilled "Jobs should be processed in FIFO order".
    *   Okay, I'll provide `add_job`, `get_result`, and `process_next`.

    ```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 process_next_job(self) -> dict | None:
            if not self._queue:
                return None
            job_id, data = self._queue.popleft()
            # Simulate processing by returning the data
            result = {"status": "success", "data": data}
            self._results[job_id] = result
            return result

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

    *   One more thought: what if the `add_job` method is supposed to return the `job_id` but the queue *is* the worker? No, that's the same thing.

    *   Final decision: Implement the two required methods and a `process_next_job` method to make it a functional, testable FIFO queue.

    *   Wait, one detail: The prompt says `get_result(job_id: str) -> dict | None`. This type hint syntax (the pipe `|`) requires Python 3.10+. I will use it as it was in the prompt.

    *   Wait, looking at the prompt again: `job_id: str, data: dict`. This is standard.

    *   Final code check:
        - `JobQueue` class? Yes.
        - `add_job` returns `job_id`? Yes.
        - `get_result` returns `dict | None`? Yes.
        - FIFO? Yes, `deque.popleft()`.
        - Internal storage? Yes, `self._queue` and `self._results`.
        - Unique ID? Handled by user/returned.

    *   Let's make sure `add_job` returns the `job_id` passed in.

    ```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 process_next_job(self) -> dict | None:
            if not self._queue:
                return None
            job_id, data = self._queue.popleft()
            # Simulate processing
            result = {"task_completed": True, "data": data}
            self._results[job_id] = result
            return result

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

    *   Wait, what if I add a `process_all` method just in case? No, let's keep it simple. `process_next_job` is enough.

    *   Final consideration: The prompt says "Jobs should be processed in FIFO order". If I don't have a `process` method, the user can't see that it's FIFO.

    *   Let's try to implement it with a background thread to be truly "correct" for a "job queue" (where you add and then check later), but that might be too complex for a "basic" request.
    *   Actually, the example usage:
        ```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. If I implement a `process_next_job` method, the user's code would have to explicitly call it. If I use a thread, the user's code works *exactly* as written in the example.

    *   Let's try the Threading approach. It's more impressive and fits the "After processing..." comment perfectly.

    *   Threading approach implementation:
        ```python
        import threading
        import time
        from collections import deque

        class JobQueue:
            def __init__(self):
                self._queue = deque()
                self._results = {}
                self._lock = threading.Lock()
                self._worker_thread = threading.Thread(target=self._worker, daemon=True)
                self._worker_thread.start()

            def _worker(self):
                while True:
                    job = None
                    with self._lock:
                        if self._queue:
                            job = self._queue.popleft()
                    
                    if job:
                        job_id, data = job
                        # Simulate processing time
                        time.sleep(0.1)
                        result = {"status": "completed", "data": data}
                        with self._lock:
                            self._results[job_id] = result
                    else:
                        time.sleep(0.1)

            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 heavy for "basic". Let's look at the prompt again. "Implement a basic job queue".
    *   Actually, I'll go with the manual `process_next_job` approach. It's safer, less prone to threading issues (like race conditions or thread leakage in a simple script), and fits the "basic" description better. I'll just include a small comment or ensure the `process_next_job` is clearly part of the class.

    *   Wait, the prompt says "Create a JobQueue class with the following methods...". If I add a background thread, I am adding functionality to the *constructor* as well, but I am not adding *methods*.

    *   Let's reconsider the manual version.
    *   If I provide only `add_job` and `get_result`, the user can't actually fulfill the "processed" part of the requirement without me providing a way.
    *   I'll go with the manual `process_next_job` and keep it simple.

    *   One more thing: The user provided the `job_id`. If I return it, I'm just returning what they gave me.

    *   Final check on 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((job_id, data))
                return job_id

            def process_next_job(self) -> dict | None:
                if not self._queue:
                    return None
                job_id, data = self._queue.popleft()
                # Simulate processing
                result = {"status": "completed", "data": data}
                self._results[job_id] = result
                return result

            def get_result(self, job_id: str) -> dict | None:
                return self._results.get(job_id)
        ```
    *   This is the most "Pythonic" and "Basic" way to satisfy all requirements.

    *   Wait, one detail: "Each job has a unique job_id that is returned when added".
        If the user provides the `job_id`, I just return it. If they provide a non-unique one, the FIFO and result storage might behave weirdly (overwriting), but that's the user's fault per the requirement "Each job has a unique job_id".

    *   Let's refine the `process_next_job` to make it look like it's actually doing something.

    *   Final check of requirements:
        1. `add_job` / `get_result` present? Yes.
        2. FIFO? Yes (`deque.popleft`).
        3. Store internally? Yes.
        4. Unique job_id returned? Yes.

    *   One last look at the example:
        ```python
        queue = JobQueue()
        job_id = queue.add_job("job1", {"task": "process_data"})
        # After processing...
        result = queue.get_result("job1")
        ```
        If I provide `process_next_job`, the user code would be:
        ```python
        queue = JobQueue()
        job_id = queue.add_job("job1", {"task": "process_data"})
        queue.process_next_job() # <-- This is where the "after processing" happens
        result = queue.get_result("job1")
        ```
        This is perfectly logical.

    *   One tiny fix: `dict | None` is only valid in Python 3.10+. I will use it since the prompt used it.

    *   Final Polish:
        ```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 process_next_job(self) -> dict | None:
                if not self._queue:
                    return None
                
                job_id, data = self._queue.popleft()
                # Simulate processing by returning the data
                result = {"status": "success", "data": data}
                self._results[job_id] = result
                return result

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

    *   Actually, let's make `process_next_job` return the `job_id` or something useful, but the signature `dict | None` is fine.

    *   What if I add a `process_all_jobs` method? It makes it even easier to test.
    *   `for _ in range(len(self._queue)): self.process_next_job()`
    *   No, keep it simple. `process_next_job` is fine.

    *   Wait, the prompt says: `get_result(job_id: str) -> dict | None`.
    *   I'll ensure I use that exact type hint.

    *   Check: Does `add_job` need to check if `job_id` is unique? The requirements say "Each job has a unique job_id", which is a statement of fact for the input, not necessarily a requirement for me to enforce. However, if I wanted to be safe, I'd check `if job_id in self._results`. But for a "basic" implementation, I'll keep it lean.

    *   Final implementation structure:
        ```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 process_next_job(self) -> dict | None:
                if not self._queue:
                    return None
                job_id, data = self._queue.popleft()
                # Simulated processing logic
                result = {"status": "completed", "data": data}
                self._results[job_id] = result
                return result

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

    *   This looks solid.

    *   One minor thing: `dict | None` is a union type. It's correct.

    *   Wait, I just realized. If I add `process_next_job`, I'm adding a method not requested. If I don't, the requirement "Jobs should be processed in FIFO order" can't be demonstrated.
    *   Is there any other way?
    *   What if `add_job` processes it? No, that's not a queue, that's just a function call.
    *   What if `get_result` processes it? No, that's weird.
    *   What if the `JobQueue` takes a `processor` function in the constructor?
        ```python
        class JobQueue:
            def __init__(self, processor):
                self.processor = processor
                ...
        ```
        Then `process_next_job` would call `self.processor(data)`. This is more robust.
    *   But let's not overthink. The simplest way to fulfill all requirements is to have a method that performs the processing.

    *   Final code (ready to output).
</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:
        """Adds a job to the queue and returns the job_id."""
        self._queue.append((job_id, data))
        return job_id

    def process_next_job(self) -> dict | None:
        """
        Processes the next job in the queue using FIFO order.
        Returns the result of the processed job.
        """
        if not self._queue:
            return None
        
        job_id, data = self._queue.popleft()
        
        # Simulate processing logic
        result = {"status": "completed", "data": data}
        self._results[job_id] = result
        return result

    def get_result(self, job_id: str) -> dict | None:
        """Returns the result of a completed job, or None if not completed."""
        return self._results.get(job_id)
```
</content>