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Embedded Machine Learning Engineer Jobs in Montana

* 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 ...

* 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 ...

* 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 ...

* 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 ...

* 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 ...

* 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 ...

* 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 ...

* 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 ...

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Embedded Machine Learning Engineer information

What does an embedded machine learning engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What are the key skills and qualifications needed to thrive as an embedded machine learning engineer?

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What are some common challenges faced by embedded machine learning engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Montana?

For Embedded Machine Learning Engineer jobs in Montana, the most frequently searched job titles are:

What job categories do people searching Embedded Machine Learning Engineer jobs in Montana look for?

The top searched job categories for Embedded Machine Learning Engineer jobs in Montana are:

What cities in Montana are hiring for Embedded Machine Learning Engineer jobs?

Cities in Montana with the most Embedded Machine Learning Engineer job openings:

Infographic showing various Embedded Machine Learning Engineer job openings in Montana as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 24% Part Time, 1% Contract, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Applied Machine Learning Engineer

Bridger Photonics

Bozeman, MT • On-site

Other

Posted 5 days ago


Job description

About Bridger Photonics, Inc.

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)