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Embedded Machine Learning Engineer Jobs in Rochester, MI

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director ... devices/embedded systems. * White-box understanding of classical ML algorithms (SVMs, HMMs ...

Senior Machine Learning Engineer

Detroit, MI · On-site +1

$126K - $180K/yr

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director ... devices/embedded systems. * White-box understanding of classical ML algorithms (SVMs, HMMs ...

* 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

See Rochester, MI salary details

$64.4K

$141.2K

$160.2K

How much do embedded machine learning engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for embedded machine learning engineer in Rochester, MI is $141,182.00, according to ZipRecruiter salary data. Most workers in this role earn between $121,000.00 and $159,200.00 per year, depending on experience, location, and employer.

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 Rochester, MI?

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

What cities near Rochester, MI are hiring for Embedded Machine Learning Engineer jobs?

Cities near Rochester, MI with the most Embedded Machine Learning Engineer job openings:

Infographic showing various Embedded Machine Learning Engineer job openings in Rochester, MI as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, 1% Contract, and 1% Nights. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $141,182 per year, or $67.9 per hour.

Data / Machine Learning Engineer - RSA - US

synergycom

Auburn Hills, MI

$108K - $130K/yr

Contractor

Posted 12 days ago


Job description

Data / Machine Learning Engineer - Application & Mainframe Modernization

Hybrid Auburn Hills, MI

Contract Role

Position Overview
We are seeking a hands-on Data / Machine Learning Engineer to support an
application and mainframe modernization initiative.
This resource will focus on developing AI/data engineering capabilities
to support the modernization effort and will work closely with a
dedicated Mainframe SME who will provide the legacy application and
mainframe expertise.
The ideal candidate will have strong hands-on experience with Python,
embeddings, vector databases, and RAG pipelines.
Key Responsibilities
- Design, develop, and support RAG (Retrieval-Augmented Generation)
pipelines for the application modernization initiative.
- Develop Python-based solutions and data/ML workflows.
- Create and work with embeddings to support retrieval and AI-driven
application use cases.
- Implement and work with vector databases to store, retrieve, and
manage embedded data.
- Build and maintain the data pipelines necessary to support RAG and
AI/ML workflows.
- Partner closely with the Mainframe SME to incorporate legacy
application knowledge and context into the modernization process.
- Work collaboratively with application and engineering teams
throughout the modernization effort.
- Test, troubleshoot, refine, and improve AI/data engineering
workflows and outputs.
- Support the development of scalable and repeatable approaches that
can be applied across the modernization initiative.

Required Qualifications
- Strong hands-on Python development experience.
- Hands-on experience building and supporting RAG pipelines.
- Experience creating and working with embeddings.
- Hands-on experience with vector databases.
- Data Engineering and/or Machine Learning Engineering experience.
- Experience developing data pipelines and integrating data across
systems.
- Strong analytical and troubleshooting skills.
- Ability to work collaboratively with technical SMEs and engineering
teams.

Preferred Qualifications
- Experience working on enterprise-scale technology initiatives.
- Experience working within complex application environments.
- Exposure to application modernization initiatives is beneficial.
Important Note
Mainframe/COBOL expertise is not required for this position. The Data/ML
Engineer will work alongside a dedicated Mainframe SME who will provide
the mainframe and legacy application expertise.
Core Skills
Python | Embeddings | Vector Databases | RAG Pipelines | Data/ML
Engineering