Applied ML Engineer: Scale ML for Global Emissions Tech

Bridger Photonics; Bozeman (MT); On-site; USD 110,000 - 170,000; Full time

Job summary

Bridger Photonics is hiring an Applied Machine Learning Engineer to join a growing ML team. You will own production models end-to-end, from dataset and feature work through training, evaluation, and validation in production, and help build agentic AI systems for internal automation and customer-facing capabilities.

You’ll collaborate with ML researchers and platform engineers to deploy models, build monitoring, and drive ML priorities across geographies.

Qualifications

• Python and ML frameworks; PyTorch preferred.
• 2+ years of production ML modeling experience.
• Git and CI/CD proficiency.
• SQL with PostgreSQL experience.
• Data lakes familiarity (Parquet, S3).
• Container deployments with Docker and Kubernetes.
• Cloud experience, preferably AWS.

Responsibilities

• Train, iterate on models in the detection pipeline for accuracy, efficiency, and generalization across geographies.
• Build and automate training and retraining workflows with Dagster, and dataset/feature pipelines on our ML platform (MLflow, DVC).
• Design and run offline experiments and evaluations to decide which model versions ship.
• Build agentic AI systems to automate internal workflows and power customer-facing capabilities.
• Collaborate with our ML research partner and platform engineers on deployment and priorities.
• Implement monitoring and observability in ML pipelines and participate in on-call rotations.

Skills

Python

PyTorch

ML pipelines

Git & CI/CD

PostgreSQL

Parquet/S3

Docker & Kubernetes

AWS

Cross-stack

Tools

Dagster

MLflow

DVC

KServe

GeoPandas

PostGIS

Kafka

Job description

Bridger Photonics is hiring an Applied Machine Learning Engineer to join a growing ML team. You will own production models end-to-end, from dataset and feature work through training, evaluation, and validation in production, and help build agentic AI systems for internal automation and customer-facing capabilities.

You’ll collaborate with ML researchers and platform engineers to deploy models, build monitoring, and drive ML priorities across geographies.