Python Developer-Federated Learning & Visualization (34663)

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. TOP Required Qualifications • Strong experience with Python software development. • Experience with machine learning, distributed systems, or data-intensive applications. • Familiarity with software engineering best practices (testing, documentation, version control). • Ability to work independently in a remote, collaborative research environment. Preferred Qualifications • 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. Job Summary 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. Key Responsibilities • 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 GitHub 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 GitHub. • Contribute to open-source governance, contribution guidelines, and developer documentation. ,