Python Programmer - Remote
DivIHN (pronounced “divine”) is a CMMI ML3-certified Technology and Talent solutions firm. Driven by a unique Purpose, Culture, and Value Delivery Model, we enable meaningful connections between talented professionals and forward-thinking organizations. Since our formation in 2002, organizations across commercial and public sectors have been trusting us to help build their teams with exceptional temporary and permanent talent.
Visit us at
https://divihn.com/find-a-job/
to learn more and view our open positions.
Please apply or call one of us to learn more
For further inquiries about this opportunity, please contact one of our Talent Specialists, Amit at
(224) 507 - 1290
(or) Abdul at (224) 507-1295
Title: Python Programmer - Remote
Location: Remote
Duration: 12 Months
Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered
This position requires U.S. Citizens only to meet the DoD requirements.
Description
1. 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.
2. 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 and 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.
3. 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.
4. 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.
5. Government-Owned Property
Yes
6. Security
No security clearance is required
About us:
DivIHN, the 'IT Asset Performance Services' organization, provides Professional Consulting, Custom Projects, and Professional Resource Augmentation services to clients in the Mid-West and beyond. The strategic characteristics of the organization are Standardization, Specialization, and Collaboration.
DivIHN is an equal opportunity employer. DivIHN does not and shall not discriminate against any employee or qualified applicant on the basis of race, color, religion (creed), gender, gender expression, age, national origin (ancestry), disability, marital status, sexual orientation, or military status.
Python, APPFL GitHub
Remote
About the Company:
DivIHN Integration Inc
Apply tot his job
Apply To this Job
Visit us at
https://divihn.com/find-a-job/
to learn more and view our open positions.
Please apply or call one of us to learn more
For further inquiries about this opportunity, please contact one of our Talent Specialists, Amit at
(224) 507 - 1290
(or) Abdul at (224) 507-1295
Title: Python Programmer - Remote
Location: Remote
Duration: 12 Months
Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered
This position requires U.S. Citizens only to meet the DoD requirements.
Description
1. 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.
2. 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 and 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.
3. 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.
4. 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.
5. Government-Owned Property
Yes
6. Security
No security clearance is required
About us:
DivIHN, the 'IT Asset Performance Services' organization, provides Professional Consulting, Custom Projects, and Professional Resource Augmentation services to clients in the Mid-West and beyond. The strategic characteristics of the organization are Standardization, Specialization, and Collaboration.
DivIHN is an equal opportunity employer. DivIHN does not and shall not discriminate against any employee or qualified applicant on the basis of race, color, religion (creed), gender, gender expression, age, national origin (ancestry), disability, marital status, sexual orientation, or military status.
Python, APPFL GitHub
Remote
About the Company:
DivIHN Integration Inc
Apply tot his job
Apply To this Job