Leah Burkhardt
Senior Software Engineer at Meta
San Francisco, California, United States
Summary
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Senior🎓
Top SchoolLeah Burkhardt is a Senior Software Engineer with 11 years of experience building scalable AI infrastructure at Meta, focused on platforms that launch and manage machine learning workflows in production. Her background blends hands-on ML research—applying deep reinforcement learning to mixed-autonomy traffic control—with systems engineering, giving her a practical edge in both model training and environment integration. She has a strong academic foundation from UC Berkeley in EECS and published work in communication and control theory, reflecting a habit of tackling technically deep problems. Leah’s contributions to open-source RL tooling (SUMO-linked environments and TRPO integration) underscore her strength in bridging simulation, training pipelines, and visualization to make research reproducible and operational. Based in San Francisco, she brings a rare mix of research rigor and production-first engineering to AI infra challenges.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Science (BS), Electrical Engineering and Computer Science, Bachelor of Science (BS), Electrical Engineering and Computer Science at University of California, BerkeleyMission San Jose High School
French, Chinese
Github Skills (11)
controls10
machine-learning10
sumo10
traffic10
congestion-control10
python10
reinforcement-learning10
github-ci4
docker4
dockers4
githubaction-workflow4
Programming languages (2)
Github contributions (5)
flow-project/flow
Oct 2016 - May 2017
Computational framework for reinforcement learning in traffic control
Role in this project:
ML Engineer
ML EngineerContributions:47 commits in 6 months
Contributions summary:Leah primarily contributed to the development of a reinforcement learning framework for traffic control. Their work involved creating a training environment using SUMO, a traffic simulation tool, and integrating it with a TRPO (Trust Region Policy Optimization) training script. The user updated the code with a SUMO-linked environment and TRPO training script and made subsequent improvements, including working on emission environment and a visualizer to display training data. This indicates a focus on model training and environment integration within the reinforcement learning context.
autonomousreinforcement-learningvehicle-controldeep-reinforcement-learningbenchmark
Lahaela/lahaela.github.io
May 2015 - Feb 2024
Contributions:2 PRs, 79 pushes, 3 branches in 8 years 9 months
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