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Nox Metals is a technology-enabled metals supplier serving aerospace and defense manufacturers. The company operates production facilities in Metro Detroit and Los Angeles, using software and automation to deliver aluminum plate, bar, and rounds faster than traditional industry practices.
You will join as a Machine Learning Engineer to build production systems that improve quoting accuracy, material selection, planning, and factory operations. This is applied ML focused on real business impact: identifying high-value opportunities, building datasets and features, establishing strong baselines, deploying models into customer and internal workflows alongside software engineers, and monitoring model quality, drift, latency, and business outcomes.
Key responsibilities include:
- Identifying high-value ML and optimization opportunities in operations
- Building datasets, features, models, evaluation systems, and production pipelines
- Establishing strong baselines before adding complexity
- Deploying models into customer and internal workflows with software engineers
- Monitoring model quality, drift, latency, and business impact
- Designing safeguards and clear fallbacks for uncertain predictions
- Working with operators and domain experts to understand constraints and failure modes
- Improving data quality and making model behavior explainable
- Documenting experiments and communicating tradeoffs clearly
The role emphasizes practical, production-ready ML. Models are evaluated on whether they improve decisions, survive real operating conditions, and earn trust from end users—not on benchmark metrics alone.
Qualifications:
- Strong applied machine learning, statistics, and software engineering foundation
- Proficiency in Python and SQL
- Rigorous approach to evaluation; skepticism of impressive-looking demos without evidence
- Ability to translate operational problems into measurable learning problems
- Comfort deploying and maintaining models in production, not just training them
- Curiosity about factories, materials, and supply chains
- Clear, practical communication; willingness to change course when 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
