Applied Machine Learning Engineer
bridger photonicsBozeman (MT)
About the role
Bridger Photonics is a technology company making a global impact on emissions reduction. Built on the foundation of our cutting-edge aerial methane detection technology, we provide industry-leading data and analytics that empower companies to reduce emissions efficiently and strategically. As we continue to expand our solutions, we remain committed to making emissions detection simple, scalable, and impactful.
Headquartered in Montana, our technology was first introduced in the USA where we quickly became a leader in methane emissions management. These results have allowed us to rapidly scale internationally. We’re a fast-growing team of innovators—from engineers and scientists to business and operations experts—dedicated to solving complex challenges. If you’re looking to apply your talents to work that enables companies making a difference, join us in shaping the future of emissions reduction.
About the role:
We are looking for an Applied Machine Learning Engineer to join our small but growing Machine Learning team. We use ML to improve the efficiency and accuracy of detecting and quantifying methane emissions, and we are actively expanding ML's role in our detection pipeline to reduce cost of goods, improve reliability, and enable the platform to scale to new geographies and customers. You’ll own production models end-to-end, from dataset and feature work through training, evaluation, and validation in production. You'll also help build the agentic AI systems we're developing for internal automation and customer-facing product capabilities.
What you'll do:
Train, iterate on, and improve the models in our detection pipeline, focusing on accuracy, efficiency, and generalization across geographies
Build and automate training and retraining workflows with Dagster, and dataset and feature pipelines on top of our ML platform (ML flow, DVC)
Design and run the offline experiments and evaluations that decide which model versions ship
Build agentic AI systems that automate internal workflows and power customer-facing product capabilities
Collaborate closely with our ML research partner on model development and our platform engineers on deployment, surfacing insights that shape ML platform and model priorities
Build monitoring and observability into ML pipelines from the start, and share on-call responsibility for production ML systemsQualificationsPython proficiency and experience with at least one ML/DL framework (PyTorch preferred)
2+ years experience training models and building or operating ML pipelines in production
Proficiency with Git and collaborative development workflows (branching, code review, CI/CD)
Experience with SQL and relational databases (PostgreSQL preferred)
Familiarity with data lake architectures and columnar storage formats (Parquet, S3)
Familiarity with containerized deployments (Docker, Kubernetes)
Experience with cloud computing providers, preferably AWS
Comfortable working across multiple layers of the tech stackPreferred QualificationsExperience with computer vision models and image datasets (familiarity with point cloud or LiDAR data is a plus)
Experience with any of: KServe, MLflow, Dagster, DVC, or similar ML tooling
Experience building LLM-based applications or agentic systems (tool use, evaluation, prompt engineering)
Experience with geospatial data tools or extensions (PostGIS, GeoPandas, GDAL)
Exposure to event-driven architectures (Kafka, CDC patterns)
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
More like this