Machine Learning Engineer II at Cambium Assessment
Boston, Massachusetts, United States
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Summary
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Julius Frost is a Machine Learning Engineer II in Boston with eight years of engineering experience and dual CS degrees (BA/MS) from Boston University. He combines academic research in deep learning and reinforcement learning with production ML work at Cambium Assessment and MORSE Corp, shipping practical fixes and enhancements to RL tooling. An active contributor to the high-profile Ray project—focused on RLlib—he’s resolved device-mismatch bugs, improved offline dataset APIs, and hardened training/rollout scripts, showing an eye for robustness in distributed ML systems. Julius is particularly interested in the societal impact of AI and seeks roles that tackle hard, meaningful problems; colleagues describe him as pragmatic, research-fluent, and effective at bridging prototypes to production.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Arts - BA, Bachelor of Arts - BA at Boston University
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:
ML Engineer
Contributions:9 reviews, 14 commits, 15 PRs in 1 year 11 months
Contributions summary:Julius primarily contributed to the RLlib library, focusing on bug fixes and enhancements within the reinforcement learning domain. They addressed issues related to device mismatches in PyTorch, corrected tuple observation space handling in SAC, and improved the input API for custom offline datasets. The user also implemented fixes for D4RL paths and made improvements to the training and rollout scripts.
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Contributions:26 commits, 23 pushes, 1 branch in 2 months
boston-universityregistrationbot
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