Sr Python Developer || Sr Data Scientist

Job Title: Sr Python Developer || Sr Data Scientist Duration: Long Term Location: Lemont IL Project Start Date: 13th March 20206 End Date for Submission: 27th Feb 2026

Scope The scope of this effort includes software engineering support for the APPFL framework. The subcontractor will contribute production-quality code, clear documentation, and sufficient testing to support new features, performance optimizations, visualization capabilities, and long-term maintainability of the framework. The work includes, but is not limited to: • Design and implementation of a real-time visualization and monitoring toolkit for federated and distributed training workflows, which can be easily integrated into APPFL. • Ongoing maintenance, bug fixes, and release support for the APPFL codebase. • Implementation of new features related to privacy-preserving federated learning. • Performance optimizations for the framework to make the framework more efficient for large scale federated training.

Objectives The objectives of this contract are to: • Develop a pluggable real-time, distributed visualization and monitoring toolkit comparable to federated or distributed versions of tools such as Weights & Biases or MLFlow, which can be easily integrated into APPFL to enhance its usability and observability. • Improve the overall robustness, performance, and scalability of the APPFL framework through ongoing maintenance, optimization, and feature development. • Ensure that APPFL remains a high-quality, well-documented, and actively maintained open-source framework suitable for production-scale federated learning in scientific and biomedical domains. • Strengthen and grow the APPFL user and developer community to support long-term sustainability, adoption, and collaborative innovation.

Tasks Task 1: Real-Time Federated Learning Visualization Toolkit The contractor shall design and implement a real-time visualization and monitoring toolkit for federated/distributed learning workflows that can be easily integrated into APPFL, APPFLx (the web application built on top of APPFL) and more general distributed training workflows. Specifically, this task includes: • Design an extensible architecture for collecting, aggregating, and visualizing FL metrics across distributed clients and servers. • Support real-time or near-real-time tracking of training progress, client system performance, and federated coordination events. • Visualize metrics such as (but not limited to): training loss/accuracy, round progression, client participation, client location, communication volume, latency, queue time, and resource utilization. • Ensure compatibility with heterogeneous execution environments (e.g., HPC, cloud, hybrid settings). • Provide clear and APIs and configuration options and write up user-facing documentation and examples demonstrating toolkit usage. • The toolkit should be designed to be modular, scalable, and suitable for open-source distribution.

Task 2: Feature Development for Privacy Preserving Federated Learning The contractor shall work collaboratively with other researchers and developers to implement new features and algorithms related to privacy preserving large-scaling federated learning, which may include: • Support for new privacy preserving mechanisms for more secure FL experiments. • Optimize the memory footprints and the communication patterns for better scalability for large-scale (in terms of both model sizes and number of clients) experiments. • Implement necessary features, such as distributed client trainers, for seamless development of foundation models for science using APPFL.

Task 3: Framework Maintenance and Release Support The contractor shall provide ongoing maintenance and release support for APPFL by: • Investigating and resolving bugs reported via GitHub issues in a timely manner. • Refactoring codebase where appropriate to improve user experience and robustness. • Updating unit tests and integration tests as needed. • Reviewing community pull requested as needed. • Ensuring new features are well documented with clear user guides, API references, and example scripts. • Assisting with version release and changelog preparation.

Task 4: Community Building and Ecosystem Development The contractor shall contribute to the growth and sustainability of the APPFL user and developer community by: • Improving public-facing documentation, tutorials, and example workflows to lower the barrier to entry. • Helping with developing reproducible example use cases suitable for demonstrations and tutorials. • Supporting issue triage and community engagement on GitHub (e.g., responding to user questions, clarifying documentation, labeling issues). • Contributing to best practices for open-source governance, contribution guidelines, and developer documentation.

Deliverables Deliverables under this contract may include: • Production-quality Python source code integrated into the APPFL GitHub repository. • A functional real-time federated learning visualization dashboard toolkit released to PyPI. • Technical documentation, user guides, and example scripts for all developed tools and features. • Contributions to various manuscripts.

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