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Embedded Machine Learning Engineer Jobs in Michigan

General Motors

Senior Machine Learning Engineer

Warren, MI · On-site +1

$222K - $227K/yr

Machine Learning Frameworks, including TensorFlow and PyTorch; Mathematical Reasoning and Probability; Programming in C++ or Python; Experience with Robot Operating System (ROS), OpenCV, or PCL;

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Embedded Machine Learning Engineer information

What are the key skills and qualifications needed to thrive as an Embedded Machine Learning Engineer, and why are they important?

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 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 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.

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Embedded Machine Learning Engineer jobs near you

Staff Machine Learning Engineer

Mariana Minerals

Ann Arbor, MI • On-site

Full-time

Posted 7 days ago

Job description

Job Summary:
Mariana Minerals is a software-first, vertically integrated minerals company on a mission to supply the critical minerals powering modern energy, AI, and defense technologies. They are seeking a Staff Machine Learning Engineer to set the technical direction for autonomous refining operations, focusing on building, validating, and optimizing control models across their facilities. This role involves solving complex modeling problems and collaborating with leadership to shape the autonomy roadmap.
Responsibilities:
• Own the autonomy roadmap across multiple circuits and facilities—deciding which unit operations to automate next and where investment in simulation and modeling pays off.
• Define how control models are validated and certified safe to deploy on real refining equipment, including how the gap between simulation and reality is measured and closed.
• Set the standards for our simulators and our modeling stack, so the whole team builds controllers that are reproducible, safe, and grounded in real project economics.
• Personally solve the hardest modeling and control problems—non-stationarity, safety constraints, and multi-objective optimization across recovery, reagent use, energy, and uptime.
• Partner with leadership on major capital and operational decisions, translating techno-economic and process insight into strategy.
• Multiply the team through technical direction, design review, and mentoring of engineers at every level—and partner with our data engineering leaders to shape the data platform the autonomy roadmap requires. You own the modeling and the on-plant outcome; they own the backbone.
Qualifications:
Required:
• 8+ years in machine learning engineering (or an exceptional 6+ with demonstrated org-level technical leadership), including production ML or control systems that ran in the real world.
• A track record of setting technical direction for ML systems in physical, industrial, robotics, or control domains.
• Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps—plus the judgment to know when a simpler approach wins.
• Demonstrated ability to de-risk ambiguous, never-been-done problems: framing the objective, the success metric, and the path for others.
• Strong cross-functional influence with both technical leadership and domain experts—chemists, metallurgists, process engineers, and geologists.
• A builder at heart. Staff engineers here still ship.
Company:
Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying the minerals critical to modern energy, AI, and defense technologies. Founded in , the company is headquartered in San Francisco, CA, US, , with a team of 51-200 employees. The company is currently Growth Stage.

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