Robin Chen
Technical Sourcer at Zoox
California, United States
Summary
🤩
Rockstar🎓
Top SchoolRobin Chen is a Technical Sourcer based in California with eight years of experience recruiting engineering talent for robotics and autonomous vehicle teams, currently supporting Zoox's mission to bring fully autonomous mobility to market. With a background recruiting across software, electrical, mechanical, and aerospace disciplines at Aerotek, Robin pairs deep hiring expertise with an ability to evaluate cross-disciplinary technical skills. Outside recruiting, Robin contributes to open-source reinforcement learning projects—implementing and refining multi-discrete policies and continuous-action support in the well-regarded OpenDILab DI-engine—bringing practical ML engineering insight to talent assessment. This blend of hands-on ML contributions and broad recruiting experience helps Robin identify candidates who can bridge research and production in autonomy and AI. Known for organizing large campus events earlier in their career, Robin also brings strong stakeholder coordination and logistics experience to complex hiring programs.
8 years of coding experience
5 years of employment as a software developer
Bachelor's of Science, Management Degree, Bachelor's of Science, Management Degree at University of California, Merced
English, Chinese, Spanish
Github Skills (9)
dqn10
pytorch10
ppp10
python10
reinforcement-learning10
machine-learning9
multi-agent-reinforcement-learning8
ata7
atari26007
Programming languages (3)
Github contributions (5)
opendilab/DI-engine
Sep 2021 - Sep 2022
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
Role in this project:
ML Engineer
ML EngineerContributions:4 reviews, 12 commits, 8 PRs in 11 months
Contributions summary:Robin primarily contributed to the implementation and refinement of reinforcement learning algorithms within the OpenDILab Decision AI Engine. Their work involved updating and integrating multi-discrete policy implementations for DQN, PPO, and Rainbow DQN models. They also added support for continuous action spaces for PPOPG, demonstrating their understanding of diverse RL methodologies. Furthermore, the user addressed bugs and optimized code within the environment management components, focusing on subprocess environments.
aireinforcement-learningmultiagent-reinforcement-learningself-playimitation-learning
opendilab/DIgging
Aug 2022 - Oct 2022
Decision Intelligence for digging best parameters in target environment.
Contributions:3 releases, 59 commits, 4 PRs in 1 month
parametersdiggingfairness-mlactive-learningdecision
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