Staff Machine Learning Engineer
Staff ML Engineer setting technical direction for autonomous mineral refining using reinforcement learning and simulation. Owns modeling, validation, and deployment of control systems on live industrial equipment.
About the job
Responsibilities
- Own the autonomy roadmap across multiple circuits and facilities—deciding which unit operations to automate next and where investment in simulation and modeling pays off
- Define how control models are validated and certified safe to deploy on real refining equipment, including how the gap between simulation and reality is measured and closed
- Set the standards for simulators and modeling stack so the team builds controllers that are reproducible, safe, and grounded in real project economics
- Personally solve the hardest modeling and control problems—non-stationarity, safety constraints, and multi-objective optimization across recovery, reagent use, energy, and uptime
- Partner with leadership on major capital and operational decisions, translating techno-economic and process insight into strategy
- Multiply the team through technical direction, design review, and mentoring of engineers at every level
- Partner with data engineering leaders to shape the data platform the autonomy roadmap requires
Requirements
- 8+ years in machine learning engineering (or an exceptional 6+ with demonstrated org-level technical leadership), including production ML or control systems that ran in the real world
- Track record of setting technical direction for ML systems in physical, industrial, robotics, or control domains
- Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps
- Demonstrated ability to de-risk ambiguous, never-been-done problems: framing the objective, the success metric, and the path for others
- Strong cross-functional influence with both technical leadership and domain experts—chemists, metallurgists, process engineers, and geologists
- Builder mindset; Staff engineers still ship
Nice-to-Haves
- Experience with physically realistic simulators of process units
- Background in industrial control systems or robotics applied to real-world equipment
Skills
Reinforcement Learning, Machine Learning, Control Systems, Simulation, Digital Twins, Sim-To-Real Transfer, Multi-Objective Optimization, Non-Stationary Systems, Production Ml, Technical Leadership
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