Applied ML Engineer: Production Decisions

Nox Metals; Los Angeles (CA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity in Nox Metals

Company-paid medical, dental, and vis­

Job summary

Nox Metals in Los Angeles, CA is seeking a Machine Learning Engineer to build production systems that speed up quoting, material selection, planning, and factory decisions. You will work with messy operational data and put useful ideas into production.

This role emphasizes applied ML that improves decisions, earns trust, and operates in real conditions. You will deploy models with software engineers, monitor drift and latency, and collaborate with domain experts to optimize data quality and

Qualifications

• Strong background in applied machine learning, statistics, and software engineering.
• Proficiency in Python and SQL.
• Rigorous evaluation and skepticism of flashy demos without evidence.
• Ability to translate operational problems into measurable learning problems.

Responsibilities

• 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.
• Collaborate with operators and domain experts to understand constraints.
• Improve data quality and make model behavior explainable.
• Document experiments and communicate tradeoffs clearly.

Skills

Applied ML

Statistics

Software engineering

Python

SQL

Model deployment

Evaluation rigour

Data-driven decisions

Factory/domain knowledge

Tools

Python

SQL

Job description

Nox Metals in Los Angeles, CA is seeking a Machine Learning Engineer to build production systems that speed up quoting, material selection, planning, and factory decisions. You will work with messy operational data and put useful ideas into production.

This role emphasizes applied ML that improves decisions, earns trust, and operates in real conditions. You will deploy models with software engineers, monitor drift and latency, and collaborate with domain experts to optimize data quality and