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 JobsAbout the Role
In this role, you will focus on MLOps, supporting cross-functional teams in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.
What You’ll Do
- Collaborate with Data Scientists and Engineers across the full ML lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient model development through cloud infrastructure and tooling
- Design, build, and maintain scalable machine learning infrastructure, including model serving (real-time and batch), training environments, and orchestration systems, with a focus on performance, scalability, and cost efficiency
- Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation
- Partner with and support Data Scientists by enabling effective use of cloud-based tools and infrastructure, and providing technical expertise across the ML lifecycle
- Collaborate with machine learning engineers to share knowledge, improve best practices, and foster a culture of continuous learning and development
- Support development and maintain monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure
- Develop, document, and communicate implementations and best practices across the data science lifecycle
- Manage and communicate cloud infrastructure costs and budgets to project stakeholders
- Stay current with GCP services and evolving best practices in Machine Learning Engineering and MLOps
- Additional tasks may be assigned
What Skills You Have
Required
- Experience in MLOps or DevOps practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworks
- Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
- Experience working with Data Scientists to deploy, scale, and operationalize machine learning models in production environments
- 3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery
- In-depth knowledge of cloud platform, preferably Google Cloud Platform services, particularly Vertex AI, BigQuery and Dataproc
- Extensive expertise with CI/CD and IaC best practices
- Extensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow and SQL
- Extensive expertise in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn)
- Experience working in Agile environments with an emphasis on iterative development and continuous delivery
Preferred
- Master’s Degree
- Proficiency in Java or other languages
- Retail experience
- E-commerce experience
- 5+ years of experience in Machine Learning
- Experience with optimization techniques and tools (e.g., Gurobi, linear programming, mixed-integer programming)
- Experience working with agent based or agentic AI systems, including orchestration of autonomous workflows or LLM-driven agents
About the Role
In this role, you will focus on MLOps, supporting cross-functional teams in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.
What You’ll Do
- Collaborate with Data Scientists and Engineers across the full ML lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient model development through cloud infrastructure and tooling
- Design, build, and maintain scalable machine learning infrastructure, including model serving (real-time and batch), training environments, and orchestration systems, with a focus on performance, scalability, and cost efficiency
- Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation
- Partner with and support Data Scientists by enabling effective use of cloud-based tools and infrastructure, and providing technical expertise across the ML lifecycle
- Collaborate with machine learning engineers to share knowledge, improve best practices, and foster a culture of continuous learning and development
- Support development and maintain monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure
- Develop, document, and communicate implementations and best practices across the data science lifecycle
- Manage and communicate cloud infrastructure costs and budgets to project stakeholders
- Stay current with GCP services and evolving best practices in Machine Learning Engineering and MLOps
- Additional tasks may be assigned
What Skills You Have
Required
- Experience in MLOps or DevOps practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworks
- Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
- Experience working with Data Scientists to deploy, scale, and operationalize machine learning models in production environments
- 3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery
- In-depth knowledge of cloud platform, preferably Google Cloud Platform services, particularly Vertex AI, BigQuery and Dataproc
- Extensive expertise with CI/CD and IaC best practices
- Extensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow and SQL
- Extensive expertise in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn)
- Experience working in Agile environments with an emphasis on iterative development and continuous delivery
Preferred
- Master’s Degree
- Proficiency in Java or other languages
- Retail experience
- E-commerce experience
- 5+ years of experience in Machine Learning
- Experience with optimization techniques and tools (e.g., Gurobi, linear programming, mixed-integer programming)
- Experience working with agent based or agentic AI systems, including orchestration of autonomous workflows or LLM-driven agents
Machine Learning Job Roles 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 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.
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