Reward Functions Developer Jobs

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1d ago

Machine Learning Engineer

Mariana Minerals Houston, TX

Houston, TX

1d ago

$120k - $180k/yr Full time Ai Ml Artificial Intelligence Automation

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Job Description

Join Mariana Minerals in Houston to build machine learning systems that help control mineral refining facilities and move toward fully autonomous refining operations.

Run reinforcement learning experiments in physically realistic simulators for mineral processing operations, then translate results into improved controllers
Build and iterate training environments, including reward functions, observations, and action logic, with guidance from senior engineers
Train and evaluate control models, monitor performance, and investigate underperformance causes
Close the sim-to-reality gap by comparing model behavior to real plant data and flagging where physics diverges
Deliver production-ready code with clean implementation and testing, contributing to services that deploy models
Collaborate with domain experts in process and chemistry to model unit operations accurately

Requirements

2-8+ years of experience in machine learning, reinforcement learning, or scientific computing (internships or research included) or a strong recent graduate with demonstrated project depth
Solid ML fundamentals with working knowledge of modern deep learning; reinforcement learning experience is a strong plus
Python proficiency and comfort reading and debugging an existing codebase
Interest in physical and industrial systems, plus eagerness to learn chemistry and process engineering from experts who will challenge assumptions
Self-starter mindset who asks good questions, ships work, and escalates blockers early

Tech and Tools

Python
Reinforcement learning
Deep learning

The Tech

Internal platform uses reinforcement learning toolkits similar to those used for self-driving vehicles and humanoid robots, applied to autonomous, short-interval control of mineral refining circuits
Models adjust operating set points and configurations in real time to optimize simultaneously across lithium recovery, reagent consumption, energy intensity, and equipment uptime
Training conditions are noisy and non-stationary: wastewater compositions shift, ore grades change, and equipment ages; models must continuously adapt
End goal is fully autonomous refining operations
Training occurs inside physically realistic simulators of process units, followed by comparison against real plant data before any live deployment

Role Details

Location: Houston, TX (onsite)
Compensation: USD 120,000 - 180,000 per year
Minimum experience: 2 years

Culture

Extreme Ownership – take full responsibility for outcomes and drive toward solutions
Engineer Out Requirements, then Automate – simplify, optimize, and automate for scale
Share Your Legos – collaborate openly and share knowledge to build bigger solutions

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