Job description

Machine Learning Engineer | Nox Metals | Los Angeles, CA 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. We will consider for employment qualified applicants with arrest or conviction records in a manner consistent with the Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance, and the California Fair Chance Act.

Requirements

Must-haves

Machine Learning Engineer | Nox Metals | Los Angeles, CA
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.
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
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-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpythonsqlai

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision