Mariana Minerals
Minerals
MachineLearningEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Machine Learning Engineer at Mariana Minerals. Run reinforcement learning experiments. Build training environments”
Industry & Context.
What They're Looking For.
Must Have
0–4 years experience, Machine learning experience, Reinforcement learning experience, Scientific computing experience, Machine learning fundamentals, Python proficiency, Debugging existing codebase
Nice to Have
Deep learning exposure, Reinforcement learning exposure, Curiosity about physical systems, Eagerness to learn chemistry, Eagerness to learn process engineering
What You'll Do.
Run reinforcement learning experiments
Build training environments
Refine reward functions
Refine observation logic
Track model performance
Interpret model performance
Compare model behavior
Flag physics divergence
Contribute to services
Understand unit operations
Full Job Description
ABOUT MARIANA MINERALS Mariana Minerals is a software-first, vertically integrated minerals company on a mission to supply the critical minerals powering modern energy, AI, and defense technologies. We’re reimagining the minerals supply chain by combining deep industry expertise with advanced software, automation, and data-driven decision-making. THE ROLE Mariana Minerals is building the critical minerals supply chain from the ground up—and we're looking for Machine Learning Engineers 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 Machine Learning Engineer at Mariana, you'll help build and improve the machine learning systems that control our mineral refining facilities. You'll start with well-scoped problems inside our simulators and training pipelines—and ramp quickly toward owning models that run on real, operating plants. Your work won't live behind dashboards or proxy metrics; you'll see its impact in real recovery rates, energy consumption, reagent usage, and uptime. THE TECH 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 comp
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