Here is the complete fixed implementation. The key improvements are:
1. Added a `threading.Lock` to synchronize access to shared dictionaries.
2. Minimized lock hold time by releasing the lock during `processor(data)` execution, which prevents blocking other threads while waiting for I/O or CPU-bound tasks.
3. Used `dict.pop()` inside the lock for a safe, atomic check-and-remove operation.

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self._lock = threading.Lock()

    def add_job(self, job_id, data):
        with self._lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Acquire lock to safely read and remove the job
        with self._lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs.pop(job_id)
            
        # Execute processor outside the lock to avoid blocking other threads
        result = processor(data)
        
        # Acquire lock to safely store the result
        with self._lock:
            self.results[job_id] = result
        return True

    def get_result(self, job_id):
        with self._lock:
            return self.results.get(job_id)
```

### Why this fixes the race condition:
- **Mutual Exclusion**: `threading.Lock` ensures that only one thread at a time can modify `self.jobs` or `self.results`.
- **Atomic Operations**: Checking existence, reading, and deleting from `self.jobs` are now performed as a single atomic block under the lock.
- **Performance**: The lock is released before calling `processor(data)`. If the processor performs I/O or heavy computation, other threads can continue adding jobs or fetching results without being unnecessarily blocked.