Machine Learning Visa Sponsorship Jobs in Wisconsin
Wisconsin's machine learning job market centers on Madison and Milwaukee, where employers like Epic Systems, Exact Sciences, and American Family Insurance actively hire ML engineers and data scientists. The state's strong university pipeline from UW-Madison and growing health tech and insurtech sectors make it a practical destination for international candidates seeking visa sponsorship in machine learning.
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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.
Machine Learning Job Roles in Wisconsin
See all Machine Learning Jobs in Wisconsin
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Search Machine Learning Jobs in WisconsinMachine Learning Jobs in Wisconsin: Frequently Asked Questions
Which companies in Wisconsin sponsor visas for machine learning roles?
Epic Systems in Verona, Exact Sciences in Madison, and American Family Insurance are among the Wisconsin employers that have sponsored work visas for technical roles including machine learning. Large healthcare organizations, insurance companies, and manufacturing firms with data science teams are the most consistent sponsors. Research-oriented employers tied to UW-Madison also hire ML talent with sponsorship support.
Which visa types are most common for machine learning jobs in Wisconsin?
The H-1B visa is the most common visa for machine learning roles in Wisconsin, as ML engineer and data scientist positions typically qualify as specialty occupations requiring a relevant bachelor's or higher degree. Some employers also sponsor O-1A visas for candidates with exceptional records in research or publications. Candidates already on OPT or STEM OPT are frequently hired before full H-1B sponsorship begins.
Which cities in Wisconsin have the most machine learning sponsorship jobs?
Madison accounts for the largest share of machine learning sponsorship activity in Wisconsin, driven by Epic Systems, Exact Sciences, the UW-Madison research ecosystem, and a growing cluster of health tech startups. Milwaukee is the second most active city, with financial services firms, manufacturing companies with predictive analytics needs, and regional enterprise employers contributing to ML hiring. Smaller opportunities exist in Green Bay and Appleton.
How to find machine learning visa sponsorship jobs in Wisconsin?
Migrate Mate is a job board built specifically for international candidates seeking visa sponsorship, and it lets you filter machine learning roles by state so you can focus on Wisconsin employers. Because sponsorship willingness varies significantly by company, filtering for confirmed sponsors saves considerable time. Migrate Mate surfaces roles from employers with a documented history of sponsoring H-1B and other work visas for ML and data science positions.
Are there any Wisconsin-specific factors that affect machine learning visa sponsorship?
Wisconsin's machine learning hiring is heavily concentrated in healthcare IT and insurance, meaning employers often require domain knowledge in those industries alongside core ML skills. UW-Madison produces a steady pipeline of ML graduates, so international candidates compete alongside strong local talent. Employers must pay H-1B holders the prevailing wage for the role and location, which is set by the Department of Labor and varies by metro area within Wisconsin.
What is the prevailing wage for sponsored machine learning 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.