Here's the complete refactored package. I've used **relative imports** for intra-package references (best practice), added proper type hints, and ensured each module has a clear, single responsibility.

### 📁 `queue/__init__.py`
```python
from .core import Job, JobQueue
from .priority import PriorityQueue

__all__ = ["Job", "JobQueue", "PriorityQueue"]
```

### 📁 `queue/core.py`
```python
import uuid
from dataclasses import dataclass, field
from typing import Any, Callable, List, Optional

@dataclass
class Job:
    """Represents a single unit of work to be processed."""
    task: Callable
    args: tuple = ()
    kwargs: dict = field(default_factory=dict)
    job_id: Optional[str] = None
    status: str = "pending"
    attempts: int = 0

    def __hash__(self):
        return hash(self.job_id) if self.job_id else super().__hash__()

class JobQueue:
    """FIFO queue for managing jobs."""
    def __init__(self):
        self._queue: List[Job] = []

    def enqueue(self, job: Job) -> str:
        if not job.job_id:
            job.job_id = str(uuid.uuid4())
        self._queue.append(job)
        return job.job_id

    def dequeue(self) -> Optional[Job]:
        if not self._queue:
            return None
        return self._queue.pop(0)

    def is_empty(self) -> bool:
        return len(self._queue) == 0

    def size(self) -> int:
        return len(self._queue)
```

### 📁 `queue/priority.py`
```python
import heapq
from typing import Optional
from .core import Job

class PriorityQueue:
    """Heap-based priority queue. Lower integer values = higher priority."""
    def __init__(self):
        self._heap: list = []
        self._counter = 0  # Ensures stable sorting for equal priorities

    def enqueue(self, job: Job, priority: int = 0) -> str:
        entry = (priority, self._counter, job)
        self._counter += 1
        heapq.heappush(self._heap, entry)
        if not job.job_id:
            import uuid
            job.job_id = str(uuid.uuid4())
        return job.job_id

    def dequeue(self) -> Optional[Job]:
        if self._heap:
            _, _, job = heapq.heappop(self._heap)
            return job
        return None

    def is_empty(self) -> bool:
        return len(self._heap) == 0

    def size(self) -> int:
        return len(self._heap)
```

### 📁 `queue/retry.py`
```python
import logging
import time
from typing import Any
from .core import Job

logger = logging.getLogger(__name__)

def process_with_retry(
    job: Job,
    max_retries: int = 3,
    base_delay: float = 1.0,
    backoff_factor: float = 2.0
) -> Any:
    """
    Execute a job with exponential backoff retry logic.
    Returns the result of the task on success, or raises after max retries.
    """
    job.attempts = 0
    
    for attempt in range(1, max_retries + 1):
        job.attempts = attempt
        job.status = "running"
        try:
            result = job.task(*job.args, **job.kwargs)
            job.status = "completed"
            return result
        except Exception as e:
            job.status = "failed"
            logger.warning(f"Attempt {attempt}/{max_retries} failed for job {job.job_id}: {e}")

            if attempt < max_retries:
                delay = base_delay * (backoff_factor ** (attempt - 1))
                logger.info(f"Retrying in {delay} seconds...")
                time.sleep(delay)

    job.status = "failed"
    logger.error(f"Job {job.job_id} permanently failed after {max_retries} attempts.")
    raise RuntimeError(f"Job {job.job_id} failed after {max_retries} attempts")
```

### 🔑 Key Design Decisions:
1. **Relative Imports**: `from .core import Job` prevents circular import issues and keeps the package self-contained.
2. **Single Responsibility**: 
   - `core.py` → Data modeling & basic FIFO operations
   - `priority.py` → Heap-based ordering logic
   - `retry.py` → Execution lifecycle & backoff strategy
3. **Public API**: `__init__.py` explicitly exposes only what consumers need, hiding internal implementation details.
4. **Type Safety**: Added `typing` annotations for better IDE support and static analysis.
5. **Error Handling**: `process_with_retry` now raises `RuntimeError` on exhaustion instead of returning `False`, following Python's "EAFP" principle and making failure paths explicit.

> ⚠️ **Note**: The package name `queue` shadows Python's standard library `queue` module. In production, consider renaming the directory to `job_queue` or `task_queue` to avoid import conflicts.