ML Engineer Visa Sponsorship Jobs in Wisconsin
Wisconsin's ML engineer hiring is anchored by companies like American Family Insurance, Epic Systems, and Exact Sciences, with Madison and Milwaukee as the primary tech hubs. Employers in insurance, health tech, and biotech regularly sponsor H-1B visas for machine learning roles, making Wisconsin a realistic target for international ML engineers seeking sponsorship.
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Get Access To All JobsMachine Learning Engineer
Job Description:
INNOVATE WITHOUT BOUNDARIES! At Milwaukee Tool we firmly believe that our People and our Culture are the secrets to our success - so we give you unlimited access to everything you need to create disruptive new technologies and solutions.
Your Role on the Team:
As a member of the Advanced Engineering and Technology (AET) Team in the Power Tool Accessories business unit you will utilize your expertise in machine learning to solve problems where no established solution exists and deliver first-of-its-kind technologies at Milwaukee Tool. You will support the research, prototyping, and delivery of ML-driven capabilities that accelerate how we design and develop products. You will take ideas from conceptual whiteboard architectures through functional prototypes and support hand-off integrations, delivering technology innovation to product and production engineering teams. This role is an individual contributor position focused on applied execution and technology demonstration, working under shared technical direction.
Why This Role is Different:
- Full‑Stack ML in a Physical Domain: Work across the ML stack, from machine and sensor-level data through model deployment on edge hardware or cloud infrastructure.
- R&D Engineering First: Apply ML across Technology Readiness Levels (TRL 1–7), bringing technology innovation to life beyond model tuning. Domain knowledge in materials, mechanics, signals, or physics is central to this role.
- Flexible Tools: Select and use frameworks and libraries best suited to the problem, without being constrained to a single ecosystem.
- Real Impact: Deliver ML-driven capabilities that shorten product development cycles and unlock new engineering possibilities at Milwaukee Tool.
What You’ll Do:
- Research and evaluate emerging AI and ML technologies, advancing them through the Technology Readiness Level (TRL) process from concept through technology integration.
- Frame engineering problems as ML problems by assessing ML value versus physics-based or analytical approaches and defining practical success criteria.
- Design, train, and evaluate ML models to help solve well-scoped applied science and engineering problems, working under the guidance of senior engineers.
- Build ML workflows spanning data acquisition, feature engineering, model development, and validation using standard scientific and ML libraries (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow).
- Support algorithm selection and the construction of standard feature sets for engineering problems.
- Support the deployment of ML models on edge hardware and cloud infrastructure, building and deploying with guidance.
- Deploy ML enabled systems on edge hardware and cloud infrastructure to support engineering decisions.
- Prepare technology transfer packages by documenting architecture decisions, known limitations, data requirements, and deployment specifications to enable technology adoption.
- Conduct experiments and data analysis following established patterns and methods; identify and debug basic model errors.
- Organize, clean, and prepare data for downstream tasks, and create visualizations that support hypotheses, insights, and conclusions.
- Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs, and support the design of data collection and test plans.
- Research and learn about emerging AI and ML technologies through literature, universities, conferences, and vendor engagement.
What You’ll Bring:
- BS in Mechanical Engineering, Electrical Engineering, Materials Science, Physics, Computer Science, Data Science, or related engineering discipline, with advanced coursework or experience in Machine Learning.
- Experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar).
- Demonstrated experience designing, training, and evaluating ML models on real-world or academic problems.
- Working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikit-learn), with familiarity with SQL.
- Exposure to at least one deep learning framework (PyTorch or TensorFlow), including training models, and awareness of cloud ML platforms (Azure ML, AWS SageMaker, or equivalent).
- Strong mathematical foundations in linear algebra, probability, statistics, and optimization, with the ability to reason about loss functions, convergence behavior, and model assumptions.
- Ability to help formulate well-scoped engineering or scientific tasks into ML problems with clear objectives and evaluation criteria, and awareness of when different model classes should be used.
- Curiosity-driven approach to learning new technologies and methods, with emphasis on applying machine learning to real-world scientific and engineering challenges.
- Ability to work across a diverse range of data types.
- Hands-on approach to collaboration and evaluation of technologies.
- Ability to thrive in an ambiguous and fast-paced environment, where problem definitions evolve.
- Ability to travel 10% of the time (domestic and international).
Preferred
- Master’s Degree in relevant field.
- Familiarity with common sensors and interpreting their physical data, and exposure to engineering test lab workflows.
- Experience with computer vision for engineering applications.
- Awareness of edge deployment concepts: model optimization and containerized deployment to industrial hardware.
- Coursework or exposure to design of experiments (DOE), uncertainty quantification, or Bayesian optimization.
- Familiarity with version control, experiment tracking, and reproducible research practices.
Working Environment
In-Person, Office Environment, R&D Engineering Lab
Our Perks and Benefits:
- Robust health, dental and vision insurance plans
- Generous 401 (K) savings plan
- Education assistance
- On-site wellness, fitness center, food, and coffee service
- And many more, check out our benefits site HERE.
Milwaukee Tool is an equal opportunity employer.
Machine Learning Engineer
Job Description:
INNOVATE WITHOUT BOUNDARIES! At Milwaukee Tool we firmly believe that our People and our Culture are the secrets to our success - so we give you unlimited access to everything you need to create disruptive new technologies and solutions.
Your Role on the Team:
As a member of the Advanced Engineering and Technology (AET) Team in the Power Tool Accessories business unit you will utilize your expertise in machine learning to solve problems where no established solution exists and deliver first-of-its-kind technologies at Milwaukee Tool. You will support the research, prototyping, and delivery of ML-driven capabilities that accelerate how we design and develop products. You will take ideas from conceptual whiteboard architectures through functional prototypes and support hand-off integrations, delivering technology innovation to product and production engineering teams. This role is an individual contributor position focused on applied execution and technology demonstration, working under shared technical direction.
Why This Role is Different:
- Full‑Stack ML in a Physical Domain: Work across the ML stack, from machine and sensor-level data through model deployment on edge hardware or cloud infrastructure.
- R&D Engineering First: Apply ML across Technology Readiness Levels (TRL 1–7), bringing technology innovation to life beyond model tuning. Domain knowledge in materials, mechanics, signals, or physics is central to this role.
- Flexible Tools: Select and use frameworks and libraries best suited to the problem, without being constrained to a single ecosystem.
- Real Impact: Deliver ML-driven capabilities that shorten product development cycles and unlock new engineering possibilities at Milwaukee Tool.
What You’ll Do:
- Research and evaluate emerging AI and ML technologies, advancing them through the Technology Readiness Level (TRL) process from concept through technology integration.
- Frame engineering problems as ML problems by assessing ML value versus physics-based or analytical approaches and defining practical success criteria.
- Design, train, and evaluate ML models to help solve well-scoped applied science and engineering problems, working under the guidance of senior engineers.
- Build ML workflows spanning data acquisition, feature engineering, model development, and validation using standard scientific and ML libraries (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow).
- Support algorithm selection and the construction of standard feature sets for engineering problems.
- Support the deployment of ML models on edge hardware and cloud infrastructure, building and deploying with guidance.
- Deploy ML enabled systems on edge hardware and cloud infrastructure to support engineering decisions.
- Prepare technology transfer packages by documenting architecture decisions, known limitations, data requirements, and deployment specifications to enable technology adoption.
- Conduct experiments and data analysis following established patterns and methods; identify and debug basic model errors.
- Organize, clean, and prepare data for downstream tasks, and create visualizations that support hypotheses, insights, and conclusions.
- Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs, and support the design of data collection and test plans.
- Research and learn about emerging AI and ML technologies through literature, universities, conferences, and vendor engagement.
What You’ll Bring:
- BS in Mechanical Engineering, Electrical Engineering, Materials Science, Physics, Computer Science, Data Science, or related engineering discipline, with advanced coursework or experience in Machine Learning.
- Experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar).
- Demonstrated experience designing, training, and evaluating ML models on real-world or academic problems.
- Working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikit-learn), with familiarity with SQL.
- Exposure to at least one deep learning framework (PyTorch or TensorFlow), including training models, and awareness of cloud ML platforms (Azure ML, AWS SageMaker, or equivalent).
- Strong mathematical foundations in linear algebra, probability, statistics, and optimization, with the ability to reason about loss functions, convergence behavior, and model assumptions.
- Ability to help formulate well-scoped engineering or scientific tasks into ML problems with clear objectives and evaluation criteria, and awareness of when different model classes should be used.
- Curiosity-driven approach to learning new technologies and methods, with emphasis on applying machine learning to real-world scientific and engineering challenges.
- Ability to work across a diverse range of data types.
- Hands-on approach to collaboration and evaluation of technologies.
- Ability to thrive in an ambiguous and fast-paced environment, where problem definitions evolve.
- Ability to travel 10% of the time (domestic and international).
Preferred
- Master’s Degree in relevant field.
- Familiarity with common sensors and interpreting their physical data, and exposure to engineering test lab workflows.
- Experience with computer vision for engineering applications.
- Awareness of edge deployment concepts: model optimization and containerized deployment to industrial hardware.
- Coursework or exposure to design of experiments (DOE), uncertainty quantification, or Bayesian optimization.
- Familiarity with version control, experiment tracking, and reproducible research practices.
Working Environment
In-Person, Office Environment, R&D Engineering Lab
Our Perks and Benefits:
- Robust health, dental and vision insurance plans
- Generous 401 (K) savings plan
- Education assistance
- On-site wellness, fitness center, food, and coffee service
- And many more, check out our benefits site HERE.
Milwaukee Tool is an equal opportunity employer.
ML Engineer Job Roles in Wisconsin
See all 113+ ML Engineer Jobs in Wisconsin
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Search ML Engineer Jobs in WisconsinML Engineer Jobs in Wisconsin: Frequently Asked Questions
Which companies sponsor visas for ML engineers in Wisconsin?
American Family Insurance and Epic Systems in Madison are among the more consistent H-1B sponsors for technical roles, including machine learning positions. Exact Sciences and Advocate Aurora Health also file for ML and data science talent. Wisconsin's insurance and health tech sectors drive most of the sponsorship activity, alongside a smaller number of manufacturing and agriculture technology firms based in the state.
Which visa types are most common for ML engineer roles in Wisconsin?
The H-1B is the most common visa for ML engineers in Wisconsin. It requires a bachelor's degree or higher in a relevant field such as computer science, statistics, or engineering, and the employer must file a Labor Condition Application with the Department of Labor. Candidates already holding OPT or STEM OPT authorization can often begin work before H-1B approval, which many Wisconsin employers accommodate.
How to find ml engineer visa sponsorship jobs in Wisconsin?
Migrate Mate filters job listings specifically by visa sponsorship availability, so you can search ML engineer roles in Wisconsin without sifting through positions that won't support international candidates. Given that sponsorship-willing employers in Wisconsin are concentrated in Madison and Milwaukee across health tech, insurance, and biotech, filtering by those industries on Migrate Mate helps narrow the search efficiently.
Which cities in Wisconsin have the most ML engineer sponsorship jobs?
Madison is Wisconsin's primary hub for ML engineering sponsorship, driven by Epic Systems, American Family Insurance, and the University of Wisconsin-Madison's research and commercialization activity. Milwaukee follows, with activity from health systems and financial services firms. Smaller but notable pockets exist in the Fox Valley corridor where manufacturing automation companies have increased their demand for machine learning talent.
Are there state-specific considerations for ML engineers pursuing sponsorship in Wisconsin?
Wisconsin's ML hiring leans heavily on health informatics, insurance analytics, and agricultural technology rather than pure software, so candidates whose experience intersects with those domains tend to be more competitive locally. The University of Wisconsin-Madison produces a steady pipeline of ML graduates, which means employer familiarity with OPT and H-1B processes is relatively high among mid-to-large Wisconsin tech employers compared to less university-proximate states.
What is the prevailing wage for sponsored ml engineer jobs in Wisconsin?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.