Here is the refactored package structure. Since the original code was conceptual (`...`), I have provided standard, functional implementations that fit the described responsibilities.

### Directory Structure

```text
queue/
├── __init__.py
├── core.py
├── priority.py
└── retry.py
```

### 1. `queue/__init__.py`
This file defines the public API. It imports the main classes and functions from internal modules, hiding the implementation details.

```python
# queue/__init__.py
from .core import Job, JobQueue
from .retry import process_with_retry

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

### 2. `queue/core.py`
Contains the primary `Job` data structure and the main `JobQueue` orchestrator. It imports the `PriorityQueue` from the `priority` module to handle sorting.

```python
# queue/core.py
from dataclasses import dataclass, field
from typing import Any, Optional, Callable
from .priority import PriorityQueue

@dataclass
class Job:
    id: str
    payload: dict
    priority: int = 0  # Higher number = higher priority
    status: str = "pending"
    created_at: str = field(default_factory=lambda: "2023-01-01T00:00:00Z")

    def __lt__(self, other):
        # Reverse comparison for max-heap behavior (higher priority first)
        return self.priority > other.priority

class JobQueue:
    def __init__(self):
        self._pq = PriorityQueue()
        self._jobs: dict[str, Job] = {}

    def add_job(self, job: Job) -> None:
        """Add a job to the queue."""
        self._jobs[job.id] = job
        self._pq.push(job)

    def get_next_job(self) -> Optional[Job]:
        """Retrieve the highest priority job."""
        job = self._pq.pop()
        if job:
            job.status = "processing"
            return job
        return None

    def complete_job(self, job_id: str) -> None:
        """Mark a job as completed."""
        if job_id in self._jobs:
            self._jobs[job_id].status = "completed"

    def mark_failed(self, job_id: str) -> None:
        """Mark a job as failed."""
        if job_id in self._jobs:
            self._jobs[job_id].status = "failed"

    def get_job(self, job_id: str) -> Optional[Job]:
        return self._jobs.get(job_id)

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

### 3. `queue/priority.py`
Handles the heap-based priority logic. It is decoupled from business logic.

```python
# queue/priority.py
import heapq
from typing import List, Optional, Any

class PriorityQueue:
    def __init__(self):
        self._heap: List[Any] = []

    def push(self, item: Any) -> None:
        """Push an item onto the priority queue."""
        # heapq is a min-heap. If Job implements __lt__ correctly,
        # we can use it directly. If we wanted strict min-heap behavior
        # regardless of item logic, we'd store (-priority, item).
        heapq.heappush(self._heap, item)

    def pop(self) -> Optional[Any]:
        """Pop the highest priority item."""
        if self._heap:
            return heapq.heappop(self._heap)
        return None

    def peek(self) -> Optional[Any]:
        """Peek at the highest priority item without removing it."""
        if self._heap:
            return self._heap[0]
        return None

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

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

### 4. `queue/retry.py`
Handles exponential backoff logic separately from queue management.

```python
# queue/retry.py
import time
import random
from typing import Callable, Any, TypeVar

T = TypeVar('T')

def process_with_retry(
    job: Any, 
    handler: Callable[[Any], T], 
    max_retries: int = 3, 
    base_delay: float = 1.0,
    max_delay: float = 60.0
) -> T:
    """
    Execute a handler with exponential backoff retry logic.
    
    Args:
        job: The job object (unused in basic logic, kept for context)
        handler: The function to execute
        max_retries: Maximum retry attempts
        base_delay: Initial delay in seconds
        max_delay: Cap the delay at this value
        
    Returns:
        The result of the handler
        
    Raises:
        Exception: If all retries are exhausted
    """
    attempt = 0
    delay = base_delay
    
    while attempt <= max_retries:
        try:
            return handler(job)
        except Exception as e:
            attempt += 1
            if attempt > max_retries:
                raise e
            
            # Exponential backoff with jitter
            delay = min(delay * 1.5, max_delay)
            jitter = random.uniform(0.5, 1.5)
            sleep_time = delay * jitter
            time.sleep(sleep_time)
            
    # Should not be reached, but for type safety
    raise RuntimeError("Retry loop failed to raise exception")
```

### Key Refactoring Decisions

1.  **Encapsulation**: `PriorityQueue` is now internal to the `queue` package. Users interact with `JobQueue`, which hides the heap implementation details.
2.  **Separation of Concerns**:
    *   `core.py` handles business entities (`Job`) and orchestration (`JobQueue`).
    *   `priority.py` handles data structure algorithms (`heapq`).
    *   `retry.py` handles error handling strategies.
3.  **Relative Imports**: All internal imports use `.` (e.g., `from .priority import ...`) to ensure the package works correctly whether imported directly or installed as a dependency.
4.  **Public API**: `__init__.py` explicitly defines `__all__`, so `from queue import ...` only pulls in intended symbols.