Machine Learning Engineer
Required skills
- machine learning
- statistics
- software engineering
- Python
- SQL
- production systems
- operational data
- evaluation
- deploying models
- maintaining models
Nice to have
- operations research
- geometric optimization
- forecasting
- pricing
- industrial data
- logistics data
Role summary
Nox Metals is seeking a Machine Learning Engineer to build production systems for quoting, material selection, planning, and factory decisions. This role involves working with operational data to develop, test, and deploy ML models that improve accuracy and speed in manufacturing. The engineer will be responsible for the full ML lifecycle, from data preparation and model building to deployment, monitoring, and ensuring model explainability and reliability. The position requires strong applied ML, statistics, and software engineering skills, with proficiency in Python and SQL. Experience in operations research, forecasting, or industrial/logistics data is a plus. This is a full-time, onsite role in Los Angeles.
About Nox Metals. American factories deserve a supply chain that moves as fast as they do. Nox Metals is building it, along with the tools that run it. We’re a technology company with operations in Metro Detroit and Los Angeles, supplying aluminum plate, bar, and rounds to aerospace and defense manufacturers. We use software and automation to supply metal to American factories faster than the industry thought possible.
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
This role is full time, in person in Los Angeles.
Nox Metals is an equal opportunity employer. We hire based on skill, attitude, and fit for the work, and we do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, height, weight, marital status, or any other protected status under federal, state, or local law.
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