5-7 years of experience in applied machine learning and software engineering
Proficiency in Python and SQL
Strong statistical analysis skills
Ability to translate operational challenges into measurable learning problems
Experience in deploying and maintaining machine learning models
Curiosity about manufacturing processes and supply chains
Strong communication skills for documenting and explaining model tradeoffs
Responsibilities
Identify high-value opportunities for machine learning and optimization
Build datasets, features, models, and production pipelines
Establish strong baselines before adding complexity
Deploy models into workflows with software engineers
Monitor model quality, drift, and business impact
Design safeguards for uncertain predictions
Collaborate with operators to understand constraints and failure modes
Improve data quality and ensure model explainability
Benefits
Equity in Nox Metals
Company-paid medical, dental, and vision insurance
Full Job 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
You are:
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
Benefits:
Equity in Nox Metals
Company-paid medical, dental, and vision insurance
