Machine Learning Engineering Manager, Chemistry & Process Engineering

Mariana Minerals • Houston, TX

4d • $176,000 - $212,000

About The Position

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 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.

Requirements

• 6+ years in machine learning or scientific computing, including 2+ years managing ML engineers or scientists with direct responsibility for hiring, performance, and growth.
• Degree or equivalent depth in chemical engineering, chemistry, materials science, or a closely related field — you can read a process flow diagram, reason about reaction kinetics and separations, and hold your own with process engineers.
• Hands-on track record shipping ML models to production for physical, scientific, or industrial systems: surrogate models, hybrid physics-ML models, time-series forecasting, Bayesian optimization, or similar.
• Strong engineering fundamentals: Python, modern ML frameworks, experiment tracking, and the discipline to make research code reproducible and maintainable.
• Track record of setting technical direction for a team and delivering against commitments in an ambiguous, cross-functional environment.
• Ability to evaluate work you didn't do — you can review a model, spot the flaw in a validation approach, and give feedback that makes the engineer better.
• Exceptional written and verbal communication across audiences, from ML engineers to process engineers to operators to executives.

Nice To Haves

• PhD in chemical engineering, chemistry, or materials science with an ML or computational focus.
• Experience with model predictive control, real-time optimization, or other closed-loop optimization of chemical or industrial processes.
• Background in mining, hydrometallurgy, energy, chemicals, or other heavy industry.
• Experience building a team from a handful of engineers to a functioning organization.

Responsibilities

• Manage, coach, and grow a team of ML engineers working on chemistry and process problems — hiring, onboarding, 1:1s, performance reviews, career development, and the hard conversations when they're needed.
• Own the technical direction of the team: model architectures, data strategy, validation methodology, and the standards for when a model is trusted enough to inform or set a process decision.
• Partner with a TPM on the roadmap: translate product priorities into scoped engineering work, push back when the ask isn't feasible, and commit to what the team will deliver.
• Work directly with process engineers, chemists, and operators to make sure the models the team builds answer the questions the plant actually has.
• Set the bar for rigor in a domain where physics matters: mass and energy balances, thermodynamic consistency, extrapolation limits, uncertainty quantification, and the difference between a model that fits and a model that's right.
• Own the production lifecycle of the team's models — deployment, monitoring, retraining, drift, and incident response — and the operational practices (on-call, runbooks, review) that keep them reliable.
• Run the team's engineering practices: code and model review, experiment tracking, reproducibility, and documentation, so work survives the person who built it.
• Stay hands-on enough to review the hardest work, unblock engineers, and prototype when the fastest path to an answer is to build it yourself.
• Own headcount planning and hiring for the team, and build the pipeline of ML engineers with chemistry and process backgrounds.

Benefits

• Everyone Gets Home Safe. We never put speed or cost ahead of people.
• Extreme Ownership. We take full responsibility for outcomes, relentlessly driving toward solutions.
• Engineer Out Requirements, then Automate. We simplify, optimize, and then automate for scale.
• Share Your Legos. We collaborate openly, share knowledge, and empower each other to build bigger, better solutions.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager