Kourosh Hakhamaneshi
Team Lead (AI) at Anyscale
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKourosh Hakhamaneshi is an AI team lead at Anyscale with eight years of experience building large-scale training and inference systems for LLMs and reinforcement learning. He leads the engineering behind serverless training and serving endpoints and has driven growth of LLM use-cases across the Anyscale ecosystem. His background includes a PhD at Berkeley AI Research focusing on unsupervised learning applied to robotics and automated design, and hands-on work refactoring Ray RLlib to make RL training stacks more modular and maintainable. Kourosh couples research rigor with production-first engineering—he’s contributed to widely used open-source projects like Ray and helped produce tutorial content that eases onboarding for practitioners. Based in Berkeley, he blends systems-level expertise in distributed ML with a knack for turning complex research ideas into practical platform features.
8 years of coding experience
2 years of employment as a software developer
PhD Electrical Engineering and Computer Science, PhD Electrical Engineering and Computer Science at University of California, BerkeleyBachelor of Science - BS Electrical and Electronics Engineering, Bachelor of Science - BS Electrical and Electronics Engineering at Sharif University of Technology
Persian, English
Stackoverflow
13reputation
138reached
0answers
1question
Github Skills (13)
pytorch10
machine-learning10
ray10
jupyter-notebook10
recommendation-system10
distribute10
python10
reinforcement-learning10
data-science10
rllib9
deep-learning9
dask6
boolean6
Programming languages (7)
Github contributions (5)
anyscale/academy
Sep 2022 - Sep 2022
Ray tutorials from Anyscale
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:29 commits, 15 PRs, 25 pushes in 4 days
Contributions summary:Kourosh's commits primarily involve modifications to a Jupyter Notebook within the `ray-rllib/acm_recsys_tutorial_2022` directory, suggesting contributions related to the tutorial content. The code changes include updates to the notebook's outline, the addition of code, and overall notebook testing, signifying the user's role in creating and refining this tutorial, which utilizes `recsim` and `rllib` in the context of a recommendation system. The user also refines the code related to the environment setup.
raypythonmachine-learning
ray-project/ray
Apr 2022 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:Back-end & ML Engineer
Back-end & ML EngineerContributions:2010 reviews, 87 commits, 381 PRs in 9 months
Contributions summary:Kourosh primarily contributed to the Ray RLlib library, focusing on enhancements and maintenance of its RLlib modules. The commits involve the removal of deprecated code and the refactoring of existing code structures in the RLlib codebase. The user's work is centered on refactoring RLlib's components, with a particular emphasis on the RLlib module and the associated code, impacting various areas such as ARS, RLlib, and other algorithms.
pythonconsistsruntimetensorflowserving
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