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# Machine Learning Engineer

- **Company:** Mariana Minerals
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, Software Debugging, Python (Programming Language), Machine Learning, Scientific Computating, Reinforcement Learning, Deep Learning, Codebase, Machine Learning Operations
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-mariana-minerals-8094598

## About the Role

* 2-8+ years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing-or a strong recent graduate with demonstrated project depth.

 * Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.

 * Proficiency in Python and comfort reading and debugging an existing codebase.

 * Curiosity about physical, industrial systems and eagerness to learn chemistry and process engineering from experts who will challenge your assumptions.

 * A self-starter who asks good questions, ships, and escalates blockers early.

## Description

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., * Build and refine pieces of our training environments-reward functions, observations, and action logic-with guidance from senior engineers.

 * Train control models, track and interpret their performance, and dig into why a model underperforms.

 * Help close the gap between simulation and reality by comparing model behavior against real plant data and flagging where the physics diverges.

 * Write clean, well-tested code and contribute to the services that put models into production.


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