Machine Learning Engineer

Build applied machine learning systems that make day-to-day manufacturing decisions faster and more reliable. Nox Metals is hiring a Machine Learning Engineer in Los Angeles, CA (onsite) to help improve quoting, material selection, planning, and factory decision-making using messy operational data. The role focuses on deployment, monitoring, safeguards, and evaluation in real operating conditions, working closely with software engineers and domain experts.

Salary: USD 151,724 - 167,695 per year.

What you’ll deliver

• Find high-value opportunities for machine learning and optimization
• Create datasets, features, models, evaluation systems, and production pipelines
• Start with strong baselines before adding complexity
• Deploy models into customer and internal workflows with help from software engineers
• Monitor model quality, drift, latency, and business impact
• Design safeguards and clear fallbacks when predictions are uncertain
• Partner 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

What you bring

• Strong applied machine learning, statistics, and software engineering skills
• Comfort working in Python and SQL
• Rigor about evaluation, with skepticism toward impressive demos that lack evidence
• Ability to translate an operational problem into a measurable learning problem
• Comfort deploying and maintaining models, not only training them
• Curiosity about factories, materials, and supply chains
• A clear, practical approach, including willingness to change direction when the data disagrees

Technologies

Python, SQL

Benefits

• Equity in Nox Metals
• Company-paid medical, dental, and vision insurance

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