ML Engineer — Environmental Forecasting & Research

Trace Air Quality, Inc; Salt Lake City, Northern (UT, KY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Trace Air Quality, Inc. is seeking a Machine Learning Engineer to advance our physics-based AI for air quality forecasting.

You will build and evaluate neural network models, contribute to research, and deploy experimental frameworks in a fast-paced startup setting. You will collaborate with product and data teams, publish internal reports, and push the boundaries of generative AI and reinforcement learning where relevant.

Qualifications

• Proficient in Python & R.
• Experience building ML models (neural networks).
• Math/statistics/computing background to support model performance.
• Familiarity with MLOps tools (Weights & Biases, MLflow, Ray, Docker).
• Education aligned with responsibilities.
• Comfort in a startup, lab, or rapid R&D environment.

Responsibilities

• Research and prototype ML models for environmental forecasting and more.
• Collaborate with team to produce technical written materials.
• Design and run experiments to evaluate novel techniques for air quality prediction and exposure risk assessment.
• Explore and apply the latest developments in generative AI, foundation models, and reinforcement learning where relevant.
• Translate ambiguous ideas into concrete research questions and system designs.
• Write clean, modular, and reproducible code (Python, PyTorch, JAX, etc.) for experimentation and deployment.
• Publish internal reports and presentations to drive technical clarity and long-term innovation strategy.
• Stay at the forefront of research in air quality modeling.

Skills

Python

R

Neural networks

Statistics

A/B testing

Education

Bachelor's degree in CS/Math or related

Tools

Weights & Biases

MLflow

Ray

Docker

Job description

Trace Air Quality, Inc. is seeking a Machine Learning Engineer to advance our physics-based AI for air quality forecasting.

You will build and evaluate neural network models, contribute to research, and deploy experimental frameworks in a fast-paced startup setting. You will collaborate with product and data teams, publish internal reports, and push the boundaries of generative AI and reinforcement learning where relevant.