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Entry Level Machine Learning Visa Sponsorship Jobs in Wisconsin

Nestle

General Laborer 3rd Shift

Little Chute, WI

$22.52/hr

... learning so that everyone can grow and thrive. This position is not eligible for Visa Sponsorship ... Production and bakery laborers are entry level positions capable of operating departmental ...

Nestle

General Laborer 1st Shift

Little Chute, WI

$22.52/hr

... learning so that everyone can grow and thrive. This position is not eligible for Visa Sponsorship ... Production and bakery laborers are entry level positions capable of operating departmental ...

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All JobsMachine Learning Visa Sponsorship JobsEntry Level Machine Learning Visa Sponsorship JobsEntry Level Machine Learning Visa Sponsorship Jobs in Wisconsin

Entry Level Machine Learning Visa Sponsorship information

What is an entry level machine learning visa sponsorship job?

Entry Level Machine Learning Visa Sponsorship jobs are positions in the field of machine learning that are suitable for candidates with little to no professional experience and are open to applicants who require employer sponsorship for a work visa. These roles typically involve assisting with data analysis, building machine learning models, and supporting senior engineers or scientists. Employers offering visa sponsorship help international candidates legally work in the country, often supporting H-1B or similar visa processes. Such jobs are common in technology companies, research labs, and startups that need fresh talent and are open to hiring globally.

What types of projects and collaboration can an entry level machine learning employee expect, especially when working under visa sponsorship?

Entry level machine learning professionals typically work on well-defined tasks such as data preprocessing, model training, and assisting with algorithm development under the guidance of senior team members. Collaboration is a key part of the role, often involving cross-functional teams including data engineers, software developers, and domain experts. Those on visa sponsorship can expect structured onboarding, mentorship opportunities, and regular feedback to support both technical growth and integration into the team. Many organizations provide clear project scopes and documentation, making it easier for new hires to ramp up and contribute effectively.

What are the key skills and qualifications needed to thrive as an entry level machine learning engineer, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid background in computer science, statistics, and mathematics, often supported by a relevant degree or coursework. Experience with programming languages like Python or R, familiarity with ML libraries (such as TensorFlow or scikit-learn), and knowledge of data processing tools are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help you collaborate and deliver impactful solutions. These skills ensure you can build, evaluate, and deploy machine learning models that address real-world challenges and support business objectives.

What is the difference between Entry Level Machine Learning Visa Sponsorship vs Entry Level Data Scientist Visa Sponsorship?

AspectEntry Level Machine Learning Visa SponsorshipEntry Level Data Scientist Visa Sponsorship
Required CredentialsBachelor's in CS, ML, or related; basic programming skillsBachelor's in CS, Statistics, or related; programming and analytical skills
Work EnvironmentResearch labs, tech companies, startupsTech firms, finance, healthcare, consulting
Industry UsageDeveloping ML models, algorithmsAnalyzing data, building predictive models
Common Search IntentVisa sponsorship for ML rolesVisa sponsorship for data science roles

Both roles require similar educational backgrounds and programming skills, often working in tech-driven environments. The main difference lies in focus: Machine Learning roles emphasize developing algorithms and models, while Data Scientist roles focus on analyzing data and deriving insights. Visa sponsorship processes are comparable, but candidates should tailor their applications to the specific role's requirements.

What are the most commonly searched types of Machine Learning Visa Sponsorship jobs in Wisconsin?

The most popular types of Machine Learning Visa Sponsorship jobs in Wisconsin are:

What are popular job titles related to Entry Level Machine Learning Visa Sponsorship jobs in Wisconsin?

For Entry Level Machine Learning Visa Sponsorship jobs in Wisconsin, the most frequently searched job titles are:

What job categories do people searching Entry Level Machine Learning Visa Sponsorship jobs in Wisconsin look for?

The top searched job categories for Entry Level Machine Learning Visa Sponsorship jobs in Wisconsin are:

Entry Level Machine Learning Visa Sponsorship jobs near you
Infographic showing various Entry Level Machine Learning Visa Sponsorship job openings in Wisconsin as of August 2026, with employment types broken down into 50% Full Time, and 50% Part Time. Highlights an 100% In-person job distribution.

Applied Machine Learning Engineer I - Advanced Engineering & Technology

Milwaukee Tool

Brookfield, WI • On-site

Full-time

Medical, Dental, Vision, Retirement

Re-posted 27 days ago

Job description

Job Description:
Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position at this time.
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.

Apply