<reasoning_content>
*   Goal: Implement a basic job queue in Python.
    *   Class: `JobQueue`.
    *   Methods:
        *   `add_job(job_id: str, data: dict) -> str`: Adds a job.
        *   `get_result(job_id: str) -> dict | None`: Returns result if job is complete.
    *   Constraint 1: FIFO processing.
    *   Constraint 2: Internal storage until processed.
    *   Constraint 3: Unique `job_id`.
    *   *Crucial Missing Detail in Requirements:* The prompt asks for the class structure but doesn't explicitly provide a "process" method in the requirement list, although it implies jobs *get* processed ("After processing..."). However, for a class to work in a self-contained way, I should probably include a way to trigger processing or simulate it.

    *Refining the Logic:*
    To make it functional and testable based on the example:
    1.  `add_job` puts the job into a queue (e.g., `collections.deque`).
    2.  We need a way to track status/results. A dictionary `self.results = {job_id: result}`.
    3.  Since the requirement doesn't specify *how* they are processed (async/multiprocessing vs manual), I'll implement a `process_next_job()` method to allow the caller to drive the processing, or simply handle the logic.
    4.  Wait, the prompt says `add_job` returns `job_id`.
    5.  Looking at the example: `job_id = queue.add_job("job1", {"task": "process_data"})`. The user provides the `job_id`? Or the method returns a new one? The signature says `add_job(job_id: str, ...)` which implies the caller provides it.

    *Wait, let's look at the signature again:*
    `add_job(job_id: str, data: dict) -> str`
    This is slightly contradictory. If I pass `job_id` in, why return it? Usually, you return a generated ID. But I will follow the signature provided.

    *Let's handle the "processing" part:*
    Since it's a "basic job queue", I'll include a `process_jobs` method to make it useful, even though it's not explicitly in the requirements list, because without it, the example `result = queue.get_result("job1")` would always return `None`.

    *Actually, a better approach for a coding task like this:*
    Implement the methods requested. Add a dummy `process_jobs` or `_process_job` to make it work.

    *Wait, let's re-read requirements strictly:*
    1. `add_job(job_id, data)`
    2. `get_result(job_id)`
    3. FIFO order.
    4. Store internally.

    To satisfy "FIFO" and "processing", I need a queue of pending jobs and a storage for results.

    *Plan:*
    - `self.queue = collections.deque()`
    - `self.results = {}`
    - `self.jobs_data = {}`
    - `add_job`: Append to deque.
    - To make it a "real" class, I'll add a `process_next_job()` method so the user can actually see the result.

    *Wait*, if I just follow the instructions *exactly*, I only implement `add_job` and `get_result`. But a queue that doesn't process anything is useless. I will add a `process_next_job` method to ensure the code is functional and follows the "After processing..." hint in the example.

    *Revised Plan:*
    ```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()
            # Simulate 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)
    ```

    Check requirements again.
    - `add_job` returns `job_id`: Yes.
    - `get_result` returns `dict | None`: Yes.
    - FIFO: Yes (using deque).
    - Internal storage: Yes.

    One detail: The requirements say `add_job(job_id: str, data: dict)`. The `job_id` is passed in.

    Let's refine the code for a clean implementation.

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

    class JobQueue:
        def __init__(self):
            self._queue = deque()
            self._results: Dict[str, Dict[str, Any]] = {}

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

        def process_next_job(self):
            """Processes the next job in the queue (FIFO)."""
            if not self._queue:
                return None

            job_id, data = self._queue.popleft()
            # Simulated processing logic
            result = {"processed": True, "data": data}
            self._results[job_id] = result
            return result

        def get_result(self, job_id: str) -> Optional[Dict[str, Any]]:
            return self._results.get(job_id)
    ```

    The prompt asks for "only the Python code, no explanations."

    Final check on requirements:
    - `add_job` signature: `(job_id: str, data: dict) -> str`. Checked.
    - `get_result` signature: `(job_id: str) -> dict | None`. Checked.
    - FIFO order: Checked (deque.popleft).
    - Unique job_id: Checked (user provides it, though in a real app it would be generated, here I follow the signature).

    Wait, if the user provides the `job_id`, and they want it to be unique, typically the class would handle generation, but the signature `add_job(job_id: str, ...)` specifically takes it as an argument. I will stick to the signature.

    One thing: In the example `job_id = queue.add_job("job1", {"task": "process_data"})`, the job_id is "job1".

    I will include `process_next_job` so the code is actually runnable/demonstratable.
</reasoning_content>

<content>
```python
from collections import deque
from typing import Dict, Any, Optional

class JobQueue:
    def __init__(self):
        self._queue = deque()
        self._results: Dict[str, Dict[str, Any]] = {}

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

    def process_next_job(self) -> Optional[Dict[str, Any]]:
        """
        Processes the next job in the queue using FIFO order.
        In a real scenario, this might run in a background thread/process.
        """
        if not self._queue:
            return None

        job_id, data = self._queue.popleft()
        
        # Simulate processing logic
        result = {
            "status": "success",
            "processed_data": data
        }
        
        self._results[job_id] = result
        return result

    def get_result(self, job_id: str) -> Optional[Dict[str, Any]]:
        """Returns the result of a completed job."""
        return self._results.get(job_id)

# Example usage:
if __name__ == "__main__":
    queue = JobQueue()
    job_id = queue.add_job("job1", {"task": "process_data"})
    
    # Simulate the processing step
    queue.process_next_job()
    
    result = queue.get_result("job1")
    print(result)
```
</content>