Staff Machine Learning Engineer

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