Machine Learning Engineer
$120,000–$160,000 year
On-site · San Francisco, California, United States or Houston, Texas, United States
Job Summary
Machine Learning Engineer to help build and improve reinforcement-learning-based control models for mineral refining facilities. Role involves running RL experiments in physically realistic simulators, building and refining training environments (reward functions, observations, and action logic), training control models, evaluating performance against real plant data, and shipping code into production. Collaboration with process and chemistry experts to understand unit operations and ensure models translate to real-world plant performance; strong emphasis on turning simulation results into tangible improvements in efficiency, energy use, and recovery rates in autonomous refining systems.
Desired Qualifications
- 0–4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing
- strong recent graduate with demonstrated project depth
- proficiency in Python
- comfort reading and debugging an existing codebase
- curiosity about physical, industrial systems and willingness to learn chemistry and process engineering
- self-starter who ships and escalates blockers early
- working knowledge of modern deep learning; exposure to reinforcement learning is a plus
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