Richard Edgar
Senior RSDE at Microsoft
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolRichard Edgar is a Senior RSDE at Microsoft and long-time maintainer of Fairlearn, bringing 6+ years of professional software engineering experience and a PhD in theoretical astrophysics. He bridges research and product, helping teams operationalize Responsible AI across AzureML and contributing to high-profile open-source projects that modernize testing and fairness tooling. His background spans HPC, CUDA, OpenMP/MPI and large-scale GPU acceleration—skills he used to cut MRI and astronomy pipelines from hours to minutes. Equally comfortable across full-stack, backend testing, and devops, he’s known for refactoring test infrastructure, improving metric pipelines, and hardening production services. Based in Cambridge, MA, he combines deep scientific computation roots with practical engineering to make AI systems more reliable and fair.
7 years of coding experience
14 years of employment as a software developer
PhD, Theoretical Astrophysics, PhD, Theoretical Astrophysics at University of Cambridge
Github Skills (25)
data-visualizations10
unit-testing10
pytest10
python10
testing10
pandas10
machine-learning10
data-visualisation10
regular-expression10
data-visualization10
unit-test10
test-automation10
data-science9
jupyter9
lib9
Programming languages (10)
Github contributions (5)
fairlearn/fairlearn
Aug 2019 - Nov 2022
A Python package to assess and improve fairness of machine learning models.
Role in this project:
ML Engineer
ML EngineerContributions:9 releases, 802 reviews, 314 commits in 3 years 3 months
Contributions summary:Richard converted existing tests to PyTest, which refactors test infrastructure to modern Python testing standards. They also addressed code style issues identified by Flake8 and created a new testing infrastructure, which refactors the test runner into a separate file that can be reused in other tests. Finally, refactored the metrics to use pandas series for performance. These changes focused on modernizing the testing process and improving code quality in the project.
machine-learning-modelspythonfairness-mlfairness-aimachine-learning
guidance-ai/guidance
Nov 2023 - May 2026
A guidance language for controlling large language models.
Role in this project:Backend Developer & Test Automation Engineer
Backend Developer & Test Automation EngineerContributions:360 reviews, 283 PRs, 382 pushes in 2 years 6 months
Contributions summary:Richard's commits primarily focused on enhancing the testing infrastructure within the `guidance-ai/guidance` repository. Their contributions included implementing and refining tests for the `gen()` function, specifically related to regular expression handling, and the interaction between generation and various language models. Furthermore, the user made improvements and refactored the existing test suite, including the integration of new metrics for tracking token counts and generating results. This work highlights a dedication to code quality, functionality, and thorough testing for the library.
large-language-models
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