# Staff Machine Learning Engineer

**Company:** [Mariana Minerals](https://hotfix.jobs/companies/mariana-minerals)
**Location:** Ann Arbor, MI
**Role:** ML Engineering
**Salary:** $160k – $200k/yr
**Experience:** 8+ years
**Skills:** Reinforcement Learning, Machine Learning, Control Systems, Simulation, Digital Twins, Sim-To-Real Transfer, Multi-Objective Optimization, Non-Stationary Systems, Production Ml, Technical Leadership
**Posted:** 2026-06-10

> Staff ML Engineer setting technical direction for autonomous mineral refining using reinforcement learning and simulation. Owns modeling, validation, and deployment of control systems on live industrial equipment.

## Job Description

## Responsibilities
- Own the autonomy roadmap across multiple circuits and facilities—deciding which unit operations to automate next and where investment in simulation and modeling pays off
- Define how control models are validated and certified safe to deploy on real refining equipment, including how the gap between simulation and reality is measured and closed
- Set the standards for simulators and modeling stack so the team builds controllers that are reproducible, safe, and grounded in real project economics
- Personally solve the hardest modeling and control problems—non-stationarity, safety constraints, and multi-objective optimization across recovery, reagent use, energy, and uptime
- Partner with leadership on major capital and operational decisions, translating techno-economic and process insight into strategy
- Multiply the team through technical direction, design review, and mentoring of engineers at every level
- Partner with data engineering leaders to shape the data platform the autonomy roadmap requires

## Requirements
- 8+ years in machine learning engineering (or an exceptional 6+ with demonstrated org-level technical leadership), including production ML or control systems that ran in the real world
- Track record of setting technical direction for ML systems in physical, industrial, robotics, or control domains
- Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps
- Demonstrated ability to de-risk ambiguous, never-been-done problems: framing the objective, the success metric, and the path for others
- Strong cross-functional influence with both technical leadership and domain experts—chemists, metallurgists, process engineers, and geologists
- Builder mindset; Staff engineers still ship

## Nice-to-Haves
- Experience with physically realistic simulators of process units
- Background in industrial control systems or robotics applied to real-world equipment

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