Staff Machine Learning Engineer

Mariana Minerals•Ann Arbor, MI

3d•$160,000 - $200,000

About The Position

Mariana Minerals is building the critical minerals supply chain from the ground up and is looking for a Staff Machine Learning Engineer to help make it autonomous. Mariana is a mining company that builds software, designing, building, commissioning, and operating its own mines and refineries. They develop proprietary chemical processes and run them at lab, pilot, and commercial scale, currently producing battery-grade lithium salts from oil and gas wastewater. The first commercial-scale lithium production facility, Lithium One, is targeting initial production in Q1 of 2027. As a Staff Machine Learning Engineer, you will set the technical direction for making refining autonomous, defining how control models are built, validated, and trusted on live equipment. You will tackle the hardest modeling problems to achieve fully autonomous operations, with your decisions impacting recovery rates, energy consumption, reagent usage, and uptime across all plants. The work involves applied AI, using reinforcement learning toolkits similar to those powering self-driving vehicles and humanoid robots, but applied to autonomous, short-interval control of mineral refining circuits. Models will adjust operating set points and configurations in real time, optimizing for lithium recovery, reagent consumption, energy intensity, and equipment uptime simultaneously. The environment is noisy and non-stationary, requiring continuous adaptation. The process involves training control models in physically realistic simulators before deploying them on live equipment.

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.
A 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—plus the judgment to know when a simpler approach wins.
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.
A builder at heart. Staff engineers here still ship.

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 our simulators and our modeling stack, so the whole 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—and partner with our data engineering leaders to shape the data platform the autonomy roadmap requires.