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Mariana Minerals
San Francisco, California, US; Houston, Texas, US; Ann Arbor, Michigan, United States
Source: Mariana Minerals careers · View original posting
From Mariana Minerals's posting. “We” and “our” refer to the employer.
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.
Mariana Minerals is a software-first, vertically integrated minerals company supplying the minerals critical to modern energy, AI, and defense technologies. Our ML systems don't live in a vacuum — they learn from and run alongside chemistry and process simulators, and they increasingly inform, and set, how our plants and chemical processes operate.
We're hiring a Machine Learning Engineering Manager to lead the team of MLEs building models for chemistry and process engineering: surrogate and hybrid models of unit operations, models that learn from plant and lab data, and the optimization and control layers that turn those models into operating decisions. You'll manage the people, own the technical quality of what ships, and partner with the Technical Product Manager for ML & Robotics on what gets built and why.
This is a player-coach role. Today this work is done by strong individual MLEs with technical leads setting direction informally. Your job is to make it a team: hire the next several engineers, set the engineering and modeling bar, and give the TPM a counterpart who can say what is technically possible, how long it will take, and what it will cost in accuracy or risk.
You spend most of your time on people, priorities, and quality — and enough time in the code and the data to keep your judgment sharp.
Turn loosely defined process problems into scoped modeling work with clear success criteria, and align process engineers, MLEs, and product behind it.
You know when a model is learning chemistry and when it's learning an artifact, and you hold the team to the former.
Give the TPM and the business honest estimates, visible tradeoffs, and early warning when something is slipping.
Understand where the team's models sit relative to the simulators, the data platform, and the control systems that consume them, so nothing falls in the gap.
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