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Embedded Machine Learning Engineer Jobs in Minneapolis, MN

drc

Machine Learning Engineer - Python

Maple Grove, MN · On-site

Machine Learning Engineer Data Recognition Corporation, Maple Grove MN Company cannot provide sponsorship for this role Please, no agencies or third parties Summary: DRC is seeking a Machine Learning ...

Shipt

Staff Machine Learning Engineer

Minneapolis, MN · On-site +1

Impact As a Staff Machine Learning Engineer on Shipt's Personalization Platform team you will drive key AI initiatives. In this role, you'll collaborate with Data Scientists to design and deploy ...

Sidmans

Senior Machine Learning Engineer

Minneapolis, MN

$109K - $149K/yr

Builds, trains and tunes machine learning models. Translates data science experiments into scalable ... Collaborate with Data Engineering on feature pipelines and data contracts. * Own production health ...

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Embedded Machine Learning Engineer information

See Minneapolis, MN salary details

$73.1K

$160.1K

$181.6K

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

As of Jun 5, 2026, the average yearly pay for embedded machine learning engineer in Minneapolis, MN is $160,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $137,300.00 and $180,600.00 per year, depending on experience, location, and employer.

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 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 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 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 Minneapolis, MN? For Embedded Machine Learning Engineer jobs in Minneapolis, MN, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Minneapolis, MN look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Minneapolis, MN are:
Embedded Machine Learning Engineer jobs near you
Infographic showing various Embedded Machine Learning Engineer job openings in Minneapolis, MN as of May 2026, with employment types broken down into 88% Full Time, 8% Part Time, and 4% Temporary. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $160,102 per year, or $77 per hour.
Machine Learning Engineer

Machine Learning Engineer

Virtusa Corporation

Minneapolis, MN • On-site

Full-time

Posted 24 days ago

Job description

Role Summary:-
Builds, trains and tunes machine learning models. Translates data science experiments into scalable, production-ready ML solutions.
Ker Responsibilities
- Translate data science prototypes into production-grade ML services and pipelines.
- Build training and inference code with reproducibility, versioning, and automated testing.
- Implement scalable model serving (online/offline), batching, and latency/throughput optimization.
- Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring).
- Collaborate with Data Engineering on feature pipelines and data contracts.
- Own production health: drift detection, performance regression, rollback strategies, and incident response.
Required Qualification:-
- 5+ years software engineering with 2+ years shipping ML models to production.
- Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch).
- Experience with containers and orchestration (Docker/Kubernetes) and API development.
- Understanding of ML system design (data leakage, training-serving skew, drift).
- CI/CD and DevOps practices applied to ML workloads (MLOps).
Nice to have:-
- Experience with feature stores, model registries, and model monitoring stacks.
- GPU optimization and distributed training experience.
- Experience with responsible AI toolkits and compliance requirements.
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About Virtusa

Sourced by ZipRecruiter

We are builders, makers, and doers with the technical skills and domain expertise to transform your business at scale and speed without disruption. Our unique Engineering First approach blends deep industry expertise and empowered, agile teams, to create holistic solutions that seamlessly move the business forward. We help clients engage with new technology paradigms to creatively build solutions that drive them to the forefront of their industries.

Industry

It services

Company size

10,000+ Employees

Headquarters location

Westborough, MA, US

Year founded

1996

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