ML Engineering Manager: Chemistry & Process Optimization

Socket.dev; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Mariana Minerals in San Francisco is seeking a Machine Learning Engineering Manager to lead a team of ML engineers building surrogate and hybrid models for chemistry and process engineering. You will hire, set engineering and modeling standards, and partner with the Technical Product Manager for ML & Robotics on what gets built and why.

This player‑coach role combines hands‑on development with leadership, overseeing deployment, monitoring, retraining, drift, and incident response to ensure

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.
• 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.
• 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

Machine learning

People management

Python

Experiment tracking

Model validation

Cross-functional collaboration

Education

Chemical engineering or related field

Tools

Python

ML frameworks

Experiment tracking

Job description

Mariana Minerals in San Francisco is seeking a Machine Learning Engineering Manager to lead a team of ML engineers building surrogate and hybrid models for chemistry and process engineering. You will hire, set engineering and modeling standards, and partner with the Technical Product Manager for ML & Robotics on what gets built and why.

This player‑coach role combines hands‑on development with leadership, overseeing deployment, monitoring, retraining, drift, and incident response to ensure