Here's the complete fixed implementation. The race condition is resolved by introducing a `threading.Lock` to protect all accesses to the shared `self.jobs` and `self.results` dictionaries. I've also minimized lock hold time by releasing the lock during the actual job processing, which prevents unnecessary thread blocking.

```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[job_id]
            del self.jobs[job_id]
            
        # Process 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)
```

### Key Fixes:
1. **Added `self.lock = threading.Lock()`** in `__init__` to provide mutual exclusion.
2. **Protected all shared state modifications** (`self.jobs` and `self.results`) with `with self.lock:` context managers.
3. **Released the lock during processing**: By extracting `data` and removing the job from `self.jobs` before calling `processor(data)`, we avoid holding the lock while the potentially slow or blocking `processor` function executes. This maintains concurrency and prevents thread starvation.
4. **Safe reads in `get_result`**: Ensures consistent reads from `self.results` even while other threads are writing.