<reasoning_content>
*   Goal: Implement a basic job queue in Python.
    *   Requirements:
        *   `JobQueue` class.
        *   `add_job(job_id, data)`: Adds job, returns `job_id`.
        *   `get_result(job_id)`: Returns result or `None`.
        *   FIFO order.
        *   Store jobs internally until processed.
        *   Unique `job_id`.
    *   Constraints: Only Python code, no explanations.

    *   The requirements mention "After processing..." in the example, implying there needs to be a way to process jobs. However, the method signature requested is only `add_job` and `get_result`. Usually, a job queue needs a worker or a `process_jobs` method to actually *do* something.
    *   Since the prompt doesn't explicitly ask for a `process` method in the required list of methods, but mentions FIFO and "After processing...", I should probably include a mechanism to process them or at least simulate it so `get_result` isn't always `None`.
    *   Wait, looking at the `add_job` signature: `add_job(job_id: str, data: dict) -> str`. The `job_id` is passed in as an argument, but the return type is `str`. Usually, if the user provides the ID, you return it.
    *   To satisfy the FIFO requirement, I'll use `collections.deque`.
    *   To store results, I'll use a dictionary.

    *   `self.queue = deque()`: to store `(job_id, data)` tuples.
    *   `self.results = {}`: to store `{job_id: result}`.
    *   `self.jobs = {}`: to keep track of jobs that are pending? Or just rely on the deque.

    *   Let's refine the logic:
        1. `add_job(job_id, data)`:
           - Store `(job_id, data)` in a deque.
           - Return `job_id`.
        2. Since the prompt doesn't provide a `process` method in the "Requirements" list, but the example implies jobs *get* processed, I will add a simple `process_next_job` method to make the code functional/testable, or I'll implement a way to simulate processing.
        3. Actually, looking closely: "Each job has a unique job_id that is returned when added". The signature `add_job(job_id: str, data: dict)` implies the caller provides the ID.
        4. Let's add a `process_job` method to actually move things from the queue to the results, even if it wasn't explicitly in the required list, because otherwise, `get_result` can never return anything other than `None`. Or, I can implement a mock processor.

    *   *Self-Correction:* If I stick strictly to the prompt's requested methods, the class is just a storage container. But a "Job Queue" implies processing. I will implement `process_next_job` so the example works.

    ```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 logic
            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)
    ```

    *   `add_job`: Returns `job_id`.
    *   `get_result`: Returns result or `None`.
    *   FIFO: `deque.popleft()` handles this.
    *   Internal storage: `_queue` and `_results`.
    *   Unique `job_id`: Provided by caller.

    *Refining the implementation for the prompt*: The prompt asks for *only* the Python code.

    Wait, the prompt says: `add_job(job_id: str, data: dict) -> str`. Usually, if you pass an ID, you don't "return" it as if the system generated it, but it's valid.

    I'll include a basic `process_next_job` method so the example usage makes sense.

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

    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) -> Optional[Dict[str, Any]]:
            if not self._queue:
                return None
            
            job_id, data = self._queue.popleft()
            # Simulate processing
            result = {"status": "success", "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)
    ```

    One small detail: "The queue should store jobs internally until they are processed". This is handled by the `deque`.

    Final check of requirements:
    1. `JobQueue` class with `add_job` and `get_result`. (Check)
    2. FIFO. (Check - `popleft`)
    3. Store jobs internally. (Check)
    4. Unique job_id returned. (Check)

    I will provide the code including the `process_next_job` method so the example actually functions.

    Wait, the user said: "Write only the Python code, no explanations."

    Let's double check the typing. `dict | None` requires Python 3.10+. I'll use `Optional` or just the pipe if I assume modern Python. I'll use the pipe to match the prompt's style.

    ```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()
            # Simulation of processing logic
            result = {"status": "completed", "processed_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, if the user's example code is:
    ```python
    queue = JobQueue()
    job_id = queue.add_job("job1", {"task": "process_data"})
    # After processing...
    result = queue.get_result("job1")
    ```
    The user doesn't call `process_next_job`. To make the code strictly match the example *without* modifying the example, I should probably allow some form of automated or external processing, but in a basic implementation, providing a method to trigger the processing is the most standard way.

    Actually, looking at the prompt again, it doesn't *require* a `process` method in the API, but it's necessary for the class to be a "Job Queue" that actually works. I'll include it.

    One more thing: The prompt says "Each job has a unique job_id that is returned when added". This is slightly redundant if the user provides the `job_id` as an argument, but I will follow the signature.

    Final Code structure:
    - imports
    - class
      - init
      - add_job
      - process_next_job (to enable the "After processing" state)
      - get_result

    Wait, I should check if I should include the example usage in the output. Usually, "Write only the Python code" implies just the implementation.

    Final plan:
    - `collections.deque` for FIFO.
    - `dict` for results.
    - `add_job` appends to deque.
    - `process_next_job` pops from deque and adds to dict.
    - `get_result` looks up in dict.
</reasoning_content>

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

class JobQueue:
    def __init__(self):
        self._queue: deque = 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 (FIFO).
        Simulates processing by returning the original data with a success status.
        """
        if not self._queue:
            return None
        
        job_id, data = self._queue.popleft()
        # Simulate processing logic
        result = {"status": "completed", "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 matching the prompt's flow:
if __name__ == "__main__":
    queue = JobQueue()
    job_id = queue.add_job("job1", {"task": "process_data"})
    
    # Simulate the "After processing..." step
    queue.process_next_job()
    
    result = queue.get_result("job1")
    print(result)
```
</content>