Machine Learning Engineer

Build applied machine learning systems that speed up and improve decisions across quoting, material selection, planning, and factory operations.

Responsibilities

• Find high-value use cases for machine learning and optimization
• Create datasets, features, models, evaluation systems, and production pipelines
• Start with strong baselines before increasing model complexity
• Deploy models into customer and internal workflows in partnership with software engineers
• Track model quality over time, including drift, latency, and business impact
• Implement safeguards and clear fallbacks when predictions are uncertain
• Collaborate 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

Requirements

• Strong foundation in applied machine learning, statistics, and software engineering
• Comfortable working in Python and SQL
• Rigorous approach to evaluation; skeptical of impressive demos without evidence
• Ability to convert an operational challenge into a measurable learning problem
• Experience deploying and maintaining models, not only training them
• Curiosity about factories, materials, and supply chains
• Clear, practical execution with willingness to change course when data contradicts assumptions

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

Location: San Francisco, CA (onsite)

Compensation: USD 151,500 - 167,500 per yearly