Machine Learning Engineer
Build and deploy reinforcement learning models to autonomously control mineral refining facilities, optimizing recovery rates, energy use, and uptime in real operating plants.
About the job
Responsibilities
- Run reinforcement learning experiments in physically realistic simulators of mineral processing operations and help turn results into better controllers
- Build and refine pieces of training environments—reward functions, observations, and action logic
- Train control models, track and interpret performance, and investigate underperformance
- Close the gap between simulation and reality by comparing model behavior against real plant data
- Write clean, well-tested code and contribute to services that put models into production
- Partner with process and chemistry experts to understand unit operations
Requirements
- 0–4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing—or a strong recent graduate with demonstrated project depth
- Solid grounding in machine learning fundamentals with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus
- Proficiency in Python and comfort reading and debugging an existing codebase
- Curiosity about physical, industrial systems and eagerness to learn chemistry and process engineering
- Self-starter who asks good questions, ships, and escalates blockers early
Nice-to-Haves
- Experience with reinforcement learning toolkits used in self-driving vehicles or humanoid robots
- Background in scientific computing or physical systems modeling
Skills
Python, Machine Learning, Deep Learning, Reinforcement Learning, Scientific Computing
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