1

Embedded Machine Learning Engineer Jobs in Minnesota

Role Summary:- Builds, trains and tunes machine learning models. Translates data science ... Required Qualification:- - 5+ years software engineering with 2+ years shipping ML models to ...

Senior Machine Learning Engineer

Saint Paul, MN ยท Hybrid

$105.40K - $144.70K/yr

Senior Machine Learning Engineer Thomson Reuters is seeking a Senior Machine Learning Engineer who will create the machine learning solutions development and technical implementation including ...

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source ...

Role Summary:- Builds, trains and tunes machine learning models. Translates data science ... Required Qualification:- - 5+ years software engineering with 2+ years shipping ML models to ...

Role Summary:- Builds, trains and tunes machine learning models. Translates data science ... Required Qualification:- - 5 years software engineering with 2 years shipping ML models to ...

Comscore, Total Visits, March 2025) Day to Day As a Machine Learning Engineer III, you will be a team lead. You will own one of the team's major workstreams, help drive technical direction for the ...

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 ...

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 ...

next page

Showing results 1-20

Embedded Machine Learning Engineer information

See Minnesota salary details

$68.6K

$150.2K

$170.4K

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

As of Jun 2, 2026, the average yearly pay for embedded machine learning engineer in Minnesota is $150,226.00, according to ZipRecruiter salary data. Most workers in this role earn between $128,800.00 and $169,400.00 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 Minnesota? For Embedded Machine Learning Engineer jobs in Minnesota, the most frequently searched job titles are:
What cities in Minnesota are hiring for Embedded Machine Learning Engineer jobs? Cities in Minnesota with the most Embedded Machine Learning Engineer job openings:
Machine Learning Engineer

Machine Learning Engineer

Virtusa

Minneapolis, MN โ€ข On-site

Other

This job post hasย expired today.ย Applications are no longer accepted.


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.


ย 

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.


ย 


Virtusa logo

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

Social media