<reasoning_content>
The user wants a basic job queue implementation in Python with specific requirements:
1. `JobQueue` class
2. `add_job(job_id: str, data: dict) -> str` method
3. `get_result(job_id: str) -> dict | None` method
4. FIFO order processing
5. Store jobs internally until processed
6. Return job_id when added

Since it's a "basic" job queue, I'll implement it using a simple in-memory structure. I'll use `collections.deque` for FIFO ordering, and a dictionary to store job results. I'll also need to simulate or actually process jobs. The prompt says "Jobs should be processed in FIFO order" and "The queue should store jobs internally until they are processed". This implies there should be some mechanism to process them. I'll add a `process_jobs()` method or process them in the background, but the prompt doesn't explicitly ask for a processing method. Wait, it says "After processing..." in the example, but doesn't specify how processing happens. I'll keep it simple: maybe a `process_next()` method, or I can just store jobs and results, and assume processing happens elsewhere, or I'll add a simple synchronous processing mechanism.

Actually, to keep it minimal and match the requirements exactly, I'll implement:
- A queue (deque) for pending jobs
- A dict for completed results
- A method to process jobs (maybe `process_jobs()` or just process them when `add_job` is called? No, FIFO implies a queue mechanism)
- I'll add a `process_jobs()` method that processes all pending jobs, or I'll make it process asynchronously in a thread, but that might be overcomplicating.
- The prompt says "Implement a basic job queue", so I'll stick to a simple synchronous version where jobs are processed when explicitly called, or I'll just store them and provide a way to process. Actually, I can just implement a simple queue that processes jobs immediately or on demand. Let's add a `process_jobs()` method that simulates processing (e.g., returns a dict with status and original data).

Wait, the requirements don't ask for a `process_jobs` method. They just say:
- `add_job`
- `get_result`
- FIFO order
- Store until processed
- Return job_id

I'll implement a simple version where jobs are stored in a deque, and there's a `process_jobs()` method that processes them in FIFO order. Or maybe I'll just use a thread pool or asyncio? The prompt says "basic", so synchronous is fine. I'll add a `process_jobs()` method for completeness, or I can just process them in the background using `threading`. But to keep it simple and deterministic, I'll add a `process_jobs()` method.

Actually, I can just implement it with a simple internal state:
```python
from collections import deque
import threading

class JobQueue:
    def __init__(self):
        self._queue = deque()
        self._results = {}
        self._lock = threading.Lock()
        self._running = False
        self._worker = None

    def add_job(self, job_id: str, data: dict) -> str:
        with self._lock:
            self._queue.append((job_id, data))
            if not self._running:
                self._running = True
                self._worker = threading.Thread(target=self._process_loop, daemon=True)
                self._worker.start()
        return job_id

    def _process_loop(self):
        while self._running or self._queue:
            with self._lock:
                if self._queue:
                    job_id, data = self._queue.popleft()
                else:
                    break
            # Simulate processing
            result = {"status": "completed", "data": data}
            with self._lock:
                self._results[job_id] = result
        self._running = False

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

But threading might be overkill for a "basic" job queue. The prompt doesn't specify async/background processing. It just says "Jobs should be processed in FIFO order" and "The queue should store jobs internally until they are processed". I'll keep it synchronous and simple. I'll add a `process_jobs()` method that processes all pending jobs, or I'll just process them one by one. Actually, I can just make `add_job` enqueue, and provide a `process_next()` or `process_all()` method. But the prompt only specifies `add_job` and `get_result`. I'll stick to those two, and maybe add a simple `_process` simulation if needed, or just assume the user will call a method to process. Wait, the example shows:
```python
queue = JobQueue()
job_id = queue.add_job("job1", {"task": "process_data"})
# After processing...
result = queue.get_result("job1")
```
It implies processing happens somewhere. I'll add a `process_jobs()` method for completeness, or I'll just make the queue process jobs in a background thread automatically. Given it's a coding test style prompt, a simple synchronous implementation with a `process_jobs()` method is usually expected. Or I can just use `queue.Queue` from standard library.

Let's stick to the simplest interpretation:
- Use `collections.deque` for FIFO
- Store jobs in a dict or deque
- Provide `add_job` and `get_result`
- Add a `process_jobs()` method to actually process them (simulate by returning a result dict)
- Use locks if needed, but for basic, maybe not necessary unless specified. I'll skip locks for simplicity unless background processing is used.

Actually, I'll implement it with a simple synchronous processing method `process_jobs()` that processes all queued jobs in FIFO order. This matches "basic job queue".

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

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

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 explicitly ask for `process_jobs`, but it's implied. I'll include it. Alternatively, I can use `threading` to process in background automatically, which is more realistic for a job queue. But "basic" often means simple. I'll stick to the synchronous version with `process_jobs()`. Actually, I can just make it process automatically in a background thread to match "until they are processed" without requiring explicit calls. But that adds complexity. I'll keep it simple.

Let's re-read carefully: "Implement a basic job queue in Python." Requirements list only `add_job` and `get_result`. I'll implement exactly that, and add a simple `process_jobs()` method for completeness. I'll also use `typing` for type hints as requested.

Actually, I can implement it with `queue.Queue` which is thread-safe and FIFO by default. But `collections.deque` is fine.

I'll write a clean, minimal implementation:

```python
from collections import deque
from typing import Optional

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

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

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

    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 `dict | None` as in the prompt. Python 3.10+ supports this.

I'll adjust to match exactly:
```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_jobs(self) -> None:
        while self._queue:
            job_id, data = self._queue.popleft()
            self._results[job_id] = {"status": "completed", "data": data}

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

This is simple and meets all requirements. The prompt doesn't explicitly ask for `process_jobs`, but it's necessary to demonstrate FIFO processing. I'll include it. Alternatively, I could use a background thread to process automatically, but that's more complex. I'll stick with this.

Wait, maybe I should use `threading` to process in background automatically so the example works without calling `process_jobs()`. The example shows:
```python
queue = JobQueue()
job_id = queue.add_job("job1", {"task": "process_data"})
# After processing...
result = queue.get_result("job1")
```
This implies processing happens automatically or asynchronously. I'll add a simple background worker using `threading` to make it work as shown.

```python
import threading
from collections import deque

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

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

    def _process_loop(self):
        while True:
            with self._lock:
                if self._queue:
                    job_id, data = self._queue.popleft()
                else:
                    import time
                    time.sleep(0.01)
                    continue
            # Simulate processing
            result = {"status": "completed", "data": data}
            with self._lock:
                self._results[job_id] = result

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

This is more realistic. But the `time.sleep` in a loop is not ideal. I can use a `threading.Event` or just keep it simple with `process_jobs()`. Given it's a "basic" job queue, I think the synchronous `process_jobs()` is acceptable and safer. I'll stick to the simpler version without threading, as it's easier to test and understand. The prompt says "basic", so I'll provide the straightforward implementation.

Actually, I'll just implement it with `queue.Queue` which is standard for this, but `deque` is fine. I'll go with the simple version. I'll add `process_jobs()` method.

Let's refine:
```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_jobs(self) -> None:
        while self._queue:
            job_id, data = self._queue.popleft()
            self._results[job_id] = {"status": "completed", "data": data}

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

This matches all requirements. I'll output just this code.
Wait, the prompt says "Write only the Python code, no explanations." I'll make sure to follow that strictly.

One minor thing: `dict | None` syntax requires Python 3.10+. I'll use it as specified in the prompt.
I'll also add type hints for other methods for completeness.

Final check of the requirements:
1. `JobQueue` class with `add_job` and `get_result` - yes
2. FIFO order - yes, using deque
3. Store jobs internally until processed - yes
4. Unique job_id returned - yes
5. Example usage matches - yes

I'll output the code now.
I'll make sure it's clean and ready.
One thing: maybe I should use `typing.Optional` for compatibility, but the prompt uses `dict | None`, so I'll stick with that.
I'll add `process_jobs` as it's necessary to fulfill "until they are processed".
Done. 
Output matches the refined version.✅
Let's generate. 
Actually, I'll remove `process_jobs` if it's not strictly required, but it's better to have it. I'll keep it.
Wait, the prompt says "The queue should store jobs internally until they are processed". It doesn't specify how they are processed. I'll keep `process_jobs`.
All good. 
Output matches. 
Proceeds. 
```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_jobs(self) -> None:
        while self._queue:
            job_id, data = self._queue.popleft()
            self._results[job_id] = {"status": "completed", "data": data}

    def get_result(self, job_id: str) -> dict | None:
        return self._results.get(job_id)
```
This looks solid. I'll output exactly this.
One minor adjustment: I'll add type hints to `__init__` and `process_jobs` for consistency, though not strictly required.
Actually, the prompt says "Write only the Python code, no explanations." I'll just provide the class.
Done. 
Self-Correction/Refinement during thought: 
Should I use `threading` to make it work exactly as the example implies (processing happens automatically)? The example has a comment `# After processing...`, which could just mean "after you call the processing method". I'll stick with the explicit `process_jobs` method as it's standard for basic queue implementations. If they wanted async, they'd specify. 
I'll output the code now. 
All good. 
Proceeds. 
[Output Generation] -> *outputs 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_jobs(self) -> None:
        while self._queue:
            job_id, data = self._queue.popleft()
            self._results[job_id] = {"status": "completed", "data": data}

    def get_result(self, job_id: str) -> dict | None:
        return self._results.get(job_id)
```
Matches requirements exactly. No extra text. Ready. 
Wait, should I include `import threading` and make it process in background to be more robust? The prompt says "basic", so synchronous is fine. I'll stick with synchronous.
Final decision: output the synchronous version. It's clean and meets all stated requirements.
Done. 
Output matches. 
[Proceeds]

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

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