Mariana Minerals is building the critical minerals supply chain from the ground up—and we’re looking for a Staff Machine Learning Engineer to help make it autonomous.
We’re not a software company selling tools to mining operators. We are a mining company that builds software. Mariana designs, builds, commissions, and operates our own mines and refineries. We develop proprietary chemical processes and run them at lab, pilot, and commercial scale. Today, we’re producing battery-grade lithium salts from real oil and gas wastewater in our facilities. Our first commercial-scale lithium production facility, Lithium One, is targeting initial production in Q1 of 2027.
As a Staff Machine Learning Engineer at Mariana, you’ll set the technical direction for how we make refining autonomous. You’ll define how control models are built, validated, and trusted on live equipment across our circuits and facilities—and you’ll personally take on the hardest modeling problems standing between us and fully autonomous operations. Your decisions will show up in real recovery rates, energy consumption, reagent usage, and uptime across every plant we run.
This is some of the most interesting applied AI work happening today.
Our internal platform, uses the same reinforcement learning toolkits that power self-driving vehicles and humanoid robots—but applied to autonomous, short-interval control of mineral refining circuits. Models adjust operating set points and configurations in real time, optimizing across lithium recovery, reagent consumption, energy intensity, and equipment uptime simultaneously.
The environment is noisy and non-stationary: wastewater compositions shift, ore grades change, equipment ages. The system must continuously adapt. The end goal is fully autonomous refining operations. When you ship here, you can literally watch the physics change.
Under the hood, that means training control models inside physically realistic simulators of our process units, then closing the gap against real plant data before anything touches live equipment.