Applied ML Engineer: Scale ML for Emissions Detection

Bridger Photonics, Inc.; Bozeman, Northern (MT, KY); Hybrid; USD 110,000 - 170,000; Full time

Job summary

Bridger Photonics, Inc. is seeking an Applied Machine Learning Engineer to join a growing ML team focused on methane detection and emissions management.

You will own end-to-end production models, from data prep to deployment, and help scale ML capabilities across geographies and customers. You will collaborate with ML researchers and platform engineers, build robust training pipelines, and contribute to agentic AI systems for internal automation and customer-facing features.

Qualifications

• Proficient in Python and ML/DL frameworks (PyTorch).
• 2+ years of experience training models and operating ML pipelines in production.
• Experience with Git and CI/CD workflows.
• Experience with SQL (PostgreSQL).
• Familiarity with data lake architectures and Parquet/S3.
• Experience with Docker and Kubernetes.
• Experience with cloud providers, preferably AWS.
• Able to work across multiple layers of the tech stack.

Responsibilities

• Train, iterate on, and improve models in the detection pipeline with a focus on accuracy and generalization across geographies.
• Build and automate training/retraining workflows on our ML platform (MLflow, DVC).
• Design offline experiments and evaluations to decide model versions for shipping.
• Develop agentic AI systems to automate internal workflows and customer-facing capabilities.
• Collaborate with ML researchers and platform engineers on deployment and prioritization.
• Build monitoring and observability into ML pipelines and participate in on-call for production systems.

Skills

Python

PyTorch

Model Training

Git/CI

PostgreSQL

Data Lake

Docker

Kubernetes

AWS

Geospatial

LiDAR

Education

BSc in CS/ML

Tools

Dagster

DVC

MLflow

KServe

PostGIS

Job description

Bridger Photonics, Inc. is seeking an Applied Machine Learning Engineer to join a growing ML team focused on methane detection and emissions management.

You will own end-to-end production models, from data prep to deployment, and help scale ML capabilities across geographies and customers. You will collaborate with ML researchers and platform engineers, build robust training pipelines, and contribute to agentic AI systems for internal automation and customer-facing features.