ML Engineering Manager: Chemistry & Process Innovation

Mariana Minerals; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Mariana Minerals is a software-first, vertically integrated minerals company powering modern energy, AI, and defense technologies. Our ML models collaborate with chemistry and process simulators to inform plant operations.

We are hiring a Machine Learning Engineering Manager to lead a team of MLEs building surrogate and hybrid models, manage people, and partner with the Technical Product Manager for ML & Robotics on what gets built and why. This is a player-coach role.

Qualifications

• 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.

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.

Skills

ML engineering

People management

Python

ML frameworks

Experiment tracking

Reproducible code

Cross-functional

Technical direction

Communication

Process engineers

Education

Chemical engineering degree

Chemistry or materials science degree

Tools

TensorFlow

PyTorch

JAX

MLflow

Job description

Mariana Minerals is a software-first, vertically integrated minerals company powering modern energy, AI, and defense technologies. Our ML models collaborate with chemistry and process simulators to inform plant operations.

We are hiring a Machine Learning Engineering Manager to lead a team of MLEs building surrogate and hybrid models, manage people, and partner with the Technical Product Manager for ML & Robotics on what gets built and why. This is a player-coach role.