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Machine Learning Engineer Jobs in Bozeman, MT (NOW HIRING)

Bespoke Labs

Machine Learning Engineer

Bozeman, MT · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Bozeman, MT

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

FICO

Solution Engineering - Lead Engineer

Bozeman, MT · On-site

$104K - $137K/yr

... Lead Engineer is to work with clients and internal teams to leverage data assets applying ... machine learning, optimization, and much more. * Credit Scoring - FICO ® Scores are used by 90 of ...

X SERIES USA

Robotic Welding Applications Engineer

Belgrade, MT · On-site

$38.25 - $52.75/hr

... machine implementation and long‑term customer success. Key Responsibilities Product & Process Learning * Develop a thorough understanding of the BeamWELD system including its welding capabilities ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Bozeman, MT

Machine Learning Engineer information

See Bozeman, MT salary details

$32.1K

$131.4K

$197.5K

How much do machine learning engineer jobs pay per year?

As of Sep 14, 2026, the average yearly pay for machine learning engineer in Bozeman, MT is $131,409.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,600.00 and $158,200.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Bozeman, MT as of September 2026, with employment types broken down into 100% Full Time. Highlights an 93% In-person, and 7% Remote job distribution, with an average salary of $131,409 per year, or $63.2 per hour.

Applied Machine Learning Engineer

Bozeman, MT • On-site

Bridger Photonics
Oil and Gas Extraction • 51 - 200 employees
Learn more about Bridger Photonicsexternal

Other

Posted 12 days ago

Job description

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 systems
Qualifications
  • Python 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 stack
Preferred Qualifications
  • Experience 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)
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