Kate Rakelly
Machine Learning Engineer at Conservation Science Partners
Portland, Oregon, United States
Summary
🤩
Rockstar🎓
Top SchoolKate Rakelly is a machine learning engineer and researcher with 11 years of experience, holding a PhD in ML from UC Berkeley and a track record of first-author papers at NeurIPS, ICML, and CoRL. She builds production-ready ML systems—from imitation learning for autonomous vehicles at Cruise to prototype LLM services and BentoML deployments at Hum—and now develops perception for autonomous electric trains while consulting on satellite image segmentation. Her research background in meta-reinforcement learning (including contributions to Berkeley’s deep RL coursework and a DeepMind internship) informs practical solutions that bridge simulation and real-world perception. Based in Portland, she’s focused on AI for social good and often moves ideas from research prototypes into containerized, deployable services.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of California, Berkeley
Spanish
Github Skills (10)
neural-network10
meta-learning10
algorithms10
machine-learning10
deep-learning10
tensorflow10
python10
reinforcement-learning10
gru9
git8
Programming languages (3)
Github contributions (5)
berkeleydeeprlcourse/homework
Nov 2018 - Nov 2018
Assignments for CS294-112.
Role in this project:
ML Engineer
ML EngineerContributions:10 commits, 5 pushes in 7 days
Contributions summary:Kate primarily contributed to the implementation and debugging of a meta-learning framework for reinforcement learning. The commits include modifications to training scripts, environment descriptions, and network architectures for a point mass environment. They addressed bugs, made code more consistent, and fixed evaluation procedures.
katerakelly/oyster
Mar 2019 - May 2020
Implementation of Efficient Off-policy Meta-learning via Probabilistic Context Variables (PEARL)
Contributions:28 commits, 1 PR, 36 pushes in 1 year 2 months
policypearlmeta-learningmetaprobabilistic
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