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# Machine Learning Engineer

- **Company:** Nox Metals
- **Location:** Los Angeles, CA, United States
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Operational Data Store, Software Engineering, Machine Learning Operations
- **Published:** October 12, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-nox-metals-10406545

## About the Role

* Strong in applied machine learning, statistics, and software engineering
 * Comfortable working in Python and SQL
 * Rigorous about evaluation and skeptical of impressive-looking demos without evidence
 * Able to turn an operational problem into a measurable learning problem
 * Comfortable deploying and maintaining models, not just training them
 * Curious about factories, materials, and supply chains
 * Clear, practical, and willing to change course when the data disagrees

Nice to have:

 * Experience with operations research, geometric optimization, forecasting, or pricing
 * Experience with industrial, logistics, or marketplace data
 * Experience building human-in-the-loop decision systems
 * Experience applying modern AI models responsibly in production

## Description

The role. We're hiring a Machine Learning Engineer to build production systems that make quoting, material selection, planning, and factory decisions faster and more accurate. You will work with messy operational data, test ideas against real outcomes, and put the useful ones into production.

This is applied ML. A model matters only when it improves a decision, survives real operating conditions, and earns the trust of the people using it.

You will:

 * Identify high-value opportunities for machine learning and optimization
 * Build datasets, features, models, evaluation systems, and production pipelines
 * Establish strong baselines before adding complexity
 * Deploy models into customer and internal workflows with software engineers
 * Monitor model quality, drift, latency, and business impact
 * Design safeguards and clear fallbacks for uncertain predictions
 * Work with operators and domain experts to understand constraints and failure modes
 * Improve data quality and make model behavior explainable
 * Document experiments and communicate tradeoffs clearly