To fix the race condition, we need to introduce a `threading.Lock` to protect access to the shared dictionaries (`self.jobs` and `self.results`). Without a lock, multiple threads can simultaneously check if a job exists, read it, and process it, leading to duplicate processing or corrupted state.

Here is the complete fixed implementation:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        # Initialize a lock to synchronize access to shared data
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        # Lock to ensure thread-safe modification of the jobs dictionary
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Lock to atomically check existence and retrieve the job
        with self.lock:
            if job_id not in self.jobs:
                return False
            # Using pop() removes the job from the queue immediately.
            # This prevents another thread from picking up the same job 
            # while this thread is processing it.
            data = self.jobs.pop(job_id)
        
        # Process the job OUTSIDE the lock.
        # This is best practice to allow other threads to add or check 
        # other jobs while this one is running.
        result = processor(data)

        # Lock to safely store the result in the results dictionary
        with self.lock:
            self.results[job_id] = result

        return True

    def get_result(self, job_id):
        # Lock to ensure thread-safe reading of the results dictionary
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes Made:
1.  **Added `self.lock`**: Created a `threading.Lock` instance in `__init__`.
2.  **Protected `add_job`**: Wrapped the dictionary assignment in a lock to prevent concurrent writes.
3.  **Protected `process_job`**:
    *   **Check and Remove**: The check (`if job_id not in self.jobs`) and the removal (`pop`) are now performed inside a lock. This ensures that a job is processed only once, even if multiple threads try to process it simultaneously.
    *   **Processing**: The actual processing logic (`processor(data)`) is moved **outside** the lock. This prevents the lock from blocking other threads while a long-running task is executing.
    *   **Result Storage**: Writing to `self.results` is wrapped in a lock to ensure data consistency.
4.  **Protected `get_result`**: Wrapped the dictionary read in a lock to prevent potential read/write conflicts.