To fix the race condition, we need to ensure that access to the shared dictionaries (`self.jobs` and `self.results`) is synchronized. We can achieve this using a `threading.Lock`.

Here is the complete fixed implementation.

**Key Changes:**
1.  **Added `self.lock`**: A lock instance is created in `__init__` to protect shared state.
2.  **Protected `add_job`**: The lock ensures that adding a job is atomic.
3.  **Protected `process_job`**:
    *   We use a lock to safely check if the job exists and retrieve its data.
    *   **Important**: We delete the job from `self.jobs` *before* releasing the lock (or immediately after retrieval). This prevents other threads from picking up the same job while it is being processed (preventing double-processing).
    *   The `processor` function is executed **outside** the lock. This is a concurrency best practice; holding a lock while performing potentially slow processing blocks other threads from adding jobs or checking results.
    *   We re-acquire the lock to safely write the result to `self.results`.
4.  **Protected `get_result`**: The lock ensures we read the result safely.

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        # Create a lock to protect shared state
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        # Lock to safely write to the jobs dictionary
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Lock to safely check existence and retrieve data
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs[job_id]
            # Remove job immediately to prevent other threads from processing it
            del self.jobs[job_id]
        
        # Process data outside the lock to avoid blocking other threads
        try:
            result = processor(data)
        except Exception:
            # Handle potential errors in the processor if necessary
            return False

        # Lock to safely write the result
        with self.lock:
            self.results[job_id] = result
        
        return True

    def get_result(self, job_id):
        # Lock to safely read from the results dictionary
        with self.lock:
            return self.results.get(job_id)
```