Sr Python Developer-W2

Myticas's direct client, based in Lemont, IL is currently seeking a Python Developer-Federated Learning & Visualization for a 100% Remote contract position.

NOTE:

Must be a US Citizen.

• Strong experience with Python software development.
• Experience with Flask/WSGI, Git Hub, Bash
• Experience with PyTorch, WandB, Pylint, Unit testing, CI/CD, and it would be a plus if familiar with any distributed training workflow (e.g., torch distributed, accelerated, deepspeed, etc.), and model inference hosting framework (e.g., vllm, ray, etc.).
• Experience with HPC, AWS/Google/Azure, Kubernetes.
• Proficiency in using cloud APIs to deploy applications and scale them is important.
• Experience with federated learning or distributed training frameworks.
• Experience building dashboards, monitoring, or visualization tools.
• Familiarity with HPC, cloud, or hybrid compute environments.
• Prior contributions to open-source projects.

Seeking a Python Software Engineer to support the development and maintenance of APPFL (Advanced Privacy-Preserving Federated Learning), an open-source framework for privacy-preserving federated learning used by national laboratories and academic research partners. In this role, you will help design and build real-time dashboards and visualization tools that allow researchers to monitor and understand distributed machine-learning workflows. You will also contribute to improving the performance, reliability, and usability of the APPFL framework while supporting its open-source community.

• Real-Time Federated Learning Visualization
  • Design and implement a real-time visualization and monitoring toolkit for federated/distributed learning workflows.
  • Build an extensible architecture to collect, aggregate, and visualize FL metrics across distributed clients and servers.
  • Support real-time or near-real-time tracking of training progress, client participation, system performance, and federated coordination events.
  • Visualize metrics such as training loss/accuracy, round progression, client participation and location, communication volume, latency, queue time, and resource utilization.
  • Ensure compatibility with HPC, cloud, and hybrid environments.
  • Provide clear APIs, configuration options, and user-facing documentation.
• Privacy-Preserving Federated Learning Features
  • Implement privacy-preserving mechanisms for secure federated learning experiments.
  • Optimize memory footprints and communication patterns for large-scale experiments (large models and many clients).
  • Develop features such as distributed client trainers to support foundation model development using APPFL.
• Framework Maintenance & Release Support
  • Investigate and resolve Git Hub issues in a timely manner.
  • Refactor the codebase to improve robustness and user experience.
  • Update unit and integration tests.
  • Review community pull requests.
  • Support version releases, changelog preparation, and documentation updates.
• Community & Ecosystem Development
  • Improve public documentation, tutorials, and example workflows.
  • Develop reproducible example use cases for demos and training.
  • Support community engagement and issue triage on Git Hub.
  • Contribute to open-source governance, contribution guidelines, and developer documentation.