Machine Learning Engineer

Mariana Minerals•Houston, TX

6d•$120,000 - $160,000

About The Position

Mariana Minerals is seeking Machine Learning Engineers to help build and improve the machine learning systems that control their mineral refining facilities. The role involves working on well-scoped problems within simulators and training pipelines, with the potential to own models that run on real, operating plants. The work directly impacts recovery rates, energy consumption, reagent usage, and equipment uptime. Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying critical minerals for modern energy, AI, and defense technologies. They are reimagining the minerals supply chain by combining industry expertise with software, automation, and data-driven decision-making. Mariana designs, builds, commissions, and operates its own mines and refineries, producing battery-grade lithium salts from oil and gas wastewater. Their first commercial-scale lithium production facility is targeting initial production in Q1 of 2027.

Requirements

0–4 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.

Responsibilities

Run reinforcement learning experiments in physically realistic simulators of mineral processing operations, and help turn the results into better controllers.
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.
Partner with process and chemistry experts to understand the unit operations you're modeling.