Machine Learning Engineer Visa Sponsorship Jobs in Wisconsin
Machine learning engineer roles in Wisconsin are concentrated in Madison and Milwaukee, where employers like Epic Systems, Northwestern Mutual, and American Family Insurance have built out data and AI teams. The state's university pipeline, anchored by UW-Madison's strong computer science and statistics programs, makes it a consistent source of international ML talent seeking visa sponsorship.
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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 Engineer Job Roles in Wisconsin
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Search Machine Learning Engineer Jobs in WisconsinMachine Learning Engineer Jobs in Wisconsin: Frequently Asked Questions
Which companies in Wisconsin sponsor visas for machine learning engineers?
Epic Systems in Verona, Northwestern Mutual and American Family Insurance in Milwaukee, and Exact Sciences in Madison are among the Wisconsin employers with established records of sponsoring H-1B visas for technical roles. Large healthcare IT and insurance firms dominate sponsorship activity in the state, as they maintain dedicated immigration support infrastructure that smaller Wisconsin employers often lack.
Which visa types are most common for machine learning engineer roles in Wisconsin?
The H-1B is the most common visa for machine learning engineers in Wisconsin, given the role's clear specialty occupation classification requiring at least a bachelor's degree in computer science, statistics, or a related field. F-1 OPT and STEM OPT extensions are also widely used by recent UW-Madison and Marquette University graduates while they secure H-1B sponsorship from their employer.
Which cities in Wisconsin have the most machine learning engineer sponsorship jobs?
Madison accounts for the largest share of ML engineering sponsorship activity in Wisconsin, driven by Epic Systems, UW-Madison's research ecosystem, and a growing cluster of health tech and data companies. Milwaukee is the second-largest hub, with financial services firms like Northwestern Mutual and ManpowerGroup actively hiring for AI and machine learning roles. Outside these two cities, sponsorship opportunities are limited.
How to find machine learning engineer visa sponsorship jobs in Wisconsin?
Migrate Mate filters job listings specifically to employers willing to sponsor visas, so you can search machine learning engineer roles in Wisconsin without sifting through positions that exclude international candidates. The platform surfaces openings at Wisconsin employers like Epic Systems and Northwestern Mutual that have active sponsorship histories, which saves significant time compared to manually researching each company's immigration support.
Are there state-specific factors that affect visa sponsorship for machine learning engineers in Wisconsin?
Wisconsin's Department of Workforce Development publishes prevailing wage data that employers must reference when filing a Labor Condition Application for H-1B machine learning engineers. Madison's concentration of research institutions and health IT firms means many ML roles intersect with government-funded projects, which can affect sponsorship timelines. UW-Madison also produces a steady pipeline of international ML graduates who are familiar with OPT-to-H-1B transition planning.
What is the prevailing wage for sponsored machine learning 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.
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