Applied ML Engineer: Factory Optimization

Nox Metals; California; On-site; USD 152,000 - 168,000; Full time

Benefits offered by this job

Equity in Nox Metals

Company-paid medical, dental, and visn

Job summary

Nox Metals in Los Angeles, CA is hiring a Machine Learning Engineer to build production systems for quoting, material selection, planning and factory decisions. You will work with messy data, test ideas against real outcomes, and deploy the most valuable approaches into production.

The role focuses on applied ML: models must improve decisions, survive in operation, and earn trust from users. Strong collaboration with software engineers and domain experts is essential.

Qualifications

• 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.

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
• 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

Skills

Applied machine learning

Statistics

Software engineering

Python

SQL

Tools

Python

SQL

Job description

Nox Metals in Los Angeles, CA is hiring a Machine Learning Engineer to build production systems for quoting, material selection, planning and factory decisions. You will work with messy data, test ideas against real outcomes, and deploy the most valuable approaches into production.

The role focuses on applied ML: models must improve decisions, survive in operation, and earn trust from users. Strong collaboration with software engineers and domain experts is essential.