<reasoning_content>
Here's a thinking process:

1.  **Understand User Requirements:**
   - Create a `JobQueue` class in Python
   - Methods:
     - `add_job(job_id: str, data: dict) -> str`: Adds a job, returns `job_id`
     - `get_result(job_id: str) -> dict | None`: Gets result of a completed job
   - FIFO processing order
   - Store jobs internally until processed
   - Each job has a unique `job_id` returned when added
   - Example usage provided
   - Output: Only Python code, no explanations

2.  **Identify Key Components:**
   - Need a queue structure for FIFO (e.g., `collections.deque`)
   - Need a storage for job results (e.g., `dict`)
   - Need a way to process jobs (the prompt says "until they are processed", but doesn't specify how processing happens. I'll need to simulate processing or provide a method to process, or just process them immediately/synchronously in a simple way. Wait, the prompt says "Jobs should be processed in FIFO order" and "The queue should store jobs internally until they are processed". It doesn't specify async/background processing. I'll implement a simple synchronous processing mechanism or just store them and have a `process_next` or similar, but the example shows `add_job` then `get_result`. To fulfill "processed in FIFO order", I should probably have a method to process jobs, or process them automatically when added? Actually, many basic job queues just store them and the user calls a method to process, or they are processed in a loop. Let's stick to a simple design: store jobs in a deque, store results in a dict. I'll add a `process_next()` or `process_all()` method, or just process them synchronously in the background? The prompt doesn't specify how processing is triggered. I'll assume synchronous processing for simplicity, or I'll just store them and provide a `process_jobs()` method. Wait, the prompt says "Jobs should be processed in FIFO order" as a requirement. I'll implement a simple synchronous processor that runs when jobs are added, or maybe just a `run()` method. Actually, a common pattern for a basic job queue in Python is to use a `deque` for pending jobs and a `dict` for results. I'll add a `process_next()` method to actually process them, or I'll just simulate processing immediately. Let's re-read carefully: "The queue should store jobs internally until they are processed". This implies there's a processing step. I'll add a `process_jobs()` method that processes all pending jobs in FIFO order. Or maybe I should just process them synchronously in a simple way. To keep it minimal and match the example, I'll just store them and have a simple processing mechanism. Actually, I can just process them immediately when added, but that violates "store internally until processed". I'll add a `process_next()` or `process_all()` method. But the example doesn't show it. Maybe the processing is implicit? I'll just simulate a simple processing step that converts data to a result. Let's assume a simple synchronous processing loop or a method to trigger processing. I'll stick to a straightforward implementation:
     - `self._queue = collections.deque()`
     - `self._results = {}`
     - `add_job` appends to queue
     - `get_result` returns from `_results`
     - I'll add a `process_pending()` method that processes jobs in FIFO order.
     - Wait, the prompt says "Jobs should be processed in FIFO order". It might be expecting a simple synchronous processing when `add_job` is called, or maybe a background thread. Given it's a "basic job queue", I'll just implement a simple synchronous processing method, or process them immediately. Actually, many coding tests for this just want the queue structure and result storage. I'll implement a simple `process_next()` or just process them in a loop. To be safe, I'll add a `process_jobs()` method that processes all pending jobs. Or I'll just process them synchronously when added? No, "store internally until they are processed" suggests deferred processing.
     - Let's look at typical implementations: They often have a `worker` thread or a `process` method. I'll keep it simple: a `process_pending()` method that pops from the deque, simulates processing (e.g., adds a "status": "completed" to the data), and stores in results.
     - Actually, I can just make the processing happen automatically in a simple way, or just leave it as a queue that stores jobs and results, and the user is expected to call a process method. I'll add `process_next()` and `process_all()`.
     - Wait, the prompt says "Write only the Python code, no explanations." I'll just implement the class with the required methods. I'll add a simple `process_pending()` method to fulfill the FIFO processing requirement. Or maybe I should just process jobs synchronously in the background using a thread? That might be overkill. I'll stick to a simple synchronous processing method.
     - Let's reconsider: Maybe the prompt expects a simple queue where `add_job` just stores it, and `get_result` returns it if processed. I'll simulate processing by having a `run()` method or just process them immediately. Actually, I'll just implement the exact methods requested, plus a simple `process_jobs()` method to handle FIFO processing. The prompt doesn't strictly require a processing method, but says "Jobs should be processed in FIFO order". I'll implement a simple synchronous processor that can be called.
     - Alternatively, I can use a `queue.Queue` from the standard library, but that's for threading. `collections.deque` is better for FIFO.
     - I'll implement:
       ```python
       import collections
       from typing import Optional

       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": job_id, "data": data})
               return job_id

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

           def get_result(self, job_id: str) -> Optional[dict]:
               return self._results.get(job_id)
       ```
     - Wait, the prompt says "get_result(job_id: str) -> dict | None". I'll use `Optional[dict]` or `dict | None` (Python 3.10+). I'll stick to `dict | None` as requested.
     - Should I auto-process? The example shows:
       ```python
       queue = JobQueue()
       job_id = queue.add_job("job1", {"task": "process_data"})
       # After processing...
       result = queue.get_result("job1")
       ```
       The comment "# After processing..." implies the user will trigger processing somehow. I'll add a `process_jobs()` method. Or maybe I should just process synchronously in a simple way. I'll stick with the above.
     - Let's refine to match exactly:
       ```python
       import collections
       from typing import Optional

       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": job_id, "data": data})
               return job_id

           def process_next(self) -> None:
               if self._queue:
                   job = self._queue.popleft()
                   self._results[job["job_id"]] = {"status": "completed", "data": job["data"]}

           def process_all(self) -> None:
               while self._queue:
                   self.process_next()

           def get_result(self, job_id: str) -> dict | None:
               return self._results.get(job_id)
       ```
     - Actually, the prompt says "Jobs should be processed in FIFO order". It doesn't specify how processing is triggered. I'll just add a `process_pending()` method. To be safe, I'll make the processing automatic via a simple synchronous loop when needed, or just provide the method. I think it's fine.
     - Wait, maybe they want a background thread? "basic job queue" usually implies synchronous or simple threading. I'll stick to synchronous `process_all()` or `process_pending()`.
     - Let's keep it minimal. I'll just implement the class with the required methods and a simple processing method. I'll use `dict | None` as return type.
     - I'll also make sure the code is clean and matches Python 3.10+ syntax for `dict | None`.
     - One more thing: The prompt says "The queue should store jobs internally until they are processed". This is satisfied by `_queue`.
     - I'll write the code now.

     Structure:
     ```python
     import collections
     from typing import Optional

     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": job_id, "data": data})
             return job_id

         def process_pending(self) -> None:
             while self._queue:
                 job = self._queue.popleft()
                 # Simulate job processing
                 self._results[job["job_id"]] = {"status": "completed", "output": job["data"]}

         def get_result(self, job_id: str) -> dict | None:
             return self._results.get(job_id)
     ```
     This looks solid. I'll adjust the processing to be more generic or just leave it as is. The prompt doesn't specify what processing does, so simulating it is fine. I'll rename `process_pending` to `process_jobs` or just keep it. Actually, I can just process them synchronously in a simple way. I'll stick with this.
     Wait, maybe I should use `queue.Queue`? No, `deque` is better for FIFO in single-threaded basic queues.
     I'll output exactly the code.
     One minor thing: `dict | None` requires Python 3.10+. I'll use it as requested.
     I'll make sure the code is self-contained.
     Final check of the prompt: "Write only the Python code, no explanations."
     I'll produce exactly that.
     I'll add a simple `process_jobs()` method to handle the FIFO processing requirement explicitly.
     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": job_id, "data": data})
             return job_id

         def process_jobs(self) -> None:
             while self._queue:
                 job = self._queue.popleft()
                 # Simulate processing the job
                 self._results[job["job_id"]] = {"status": "completed", "result": job["data"]}

         def get_result(self, job_id: str) -> dict | None:
             return self._results.get(job_id)
     ```
     This meets all requirements. FIFO is handled by deque popleft. Jobs stored until processed. Unique job_id returned. get_result returns dict or None.
     I'll output this.✅
     One thing: The prompt says `get_result(job_id: str) -> dict | None`. I'll keep that signature.
     All good.
     Output matches request.✅
     Proceeds. 
     Self-Correction/Refinement during thought: 
     Should I make processing automatic? The example shows `# After processing...` which implies the user calls something. I'll keep `process_jobs()`. It's standard.
     I'll output the code exactly.✅
     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": job_id, "data": data})
             return job_id

         def process_jobs(self) -> None:
             while self._queue:
                 job = self._queue.popleft()
                 self._results[job["job_id"]] = {"status": "completed", "output": job["data"]}

         def get_result(self, job_id: str) -> dict | None:
             return self._results.get(job_id)
     ```
     This is clean and meets all specs. I'll output it. 
     Note: I'll remove the `import collections` if I use `list.pop(0)` but `deque` is better for FIFO. I'll keep `collections.deque`.
     All good.✅
     Output matches. 
     Proceeds. 
     [Output Generation] -> *Proceeds*
     *(Self-Correction/Verification during generation prep)*: 
     The prompt says "Jobs should be processed in FIFO order". Using `collections.deque` with `popleft()` ensures FIFO. 
     The prompt says "The queue should store jobs internally until they are processed". `_queue` does this.
     The prompt says "Each job has a unique job_id that is returned when added". `add_job` returns `job_id`.
     The prompt says `get_result` returns `dict | None`. Handled.
     Everything matches. I'll output the code block only.✅
     Final check of the 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": job_id, "data": data})
             return job_id

         def process_jobs(self) -> None:
             while self._queue:
                 job = self._queue.popleft()
                 self._results[job["job_id"]] = {"status": "completed", "result": job["data"]}

         def get_result(self, job_id: str) -> dict | None:
             return self._results.get(job_id)
     ```
     Looks good. Output matches request.✅
     I will generate it now. 
     [Output] -> *Proceeds*

</reasoning_content>

<content>
```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": job_id, "data": data})
        return job_id

    def process_jobs(self) -> None:
        while self._queue:
            job = self._queue.popleft()
            # Simulate job processing
            self._results[job["job_id"]] = {"status": "completed", "output": job["data"]}

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