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Machine Learning Engineer Intern Jobs in Milwaukee, WI

Varsity Tutors

Machine Learning Tutor

Milwaukee, WI · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Hayes Performance Systems

Test Engineering Intern

Mequon, WI · On-site

$15 - $19.25/hr

... in complex machining and precision assembly. At Hayes, we don't just make parts - we make ... Able to commute to our Mequon, WI location You'll thrive in this role if you enjoy learning by ...

Generac

Principal Data & AI Architect

Waukesha, WI · On-site

Relevant certifications such as AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate. Work Experience

New

Deloitte

Senior AI Engineer - SFL Scientific

Milwaukee, WI · On-site

$103K - $141K/yr

Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...

Barry-Wehmiller

Mechanical Engineering Intern

Waukesha, WI · On-site

$18.75 - $25.25/hr

Customers rely on our machines to produce and convert items such as corrugated boxes, folding ... We are seeking a motivated and detail-oriented Mechanical Engineering Intern for Summer 2026. This ...

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Showing results 21-40

All JobsMachine Learning Engineer Intern JobsMachine Learning Engineer Intern Jobs in Milwaukee, WI

Machine Learning Engineer Intern information

See Milwaukee, WI salary details

$25.1K

$41.9K

$86.6K

How much do machine learning engineer intern jobs pay per year?

As of Aug 19, 2026, the average yearly pay for machine learning engineer intern in Milwaukee, WI is $41,919.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,000.00 and $45,300.00 per year, depending on experience, location, and employer.

What is a machine learning engineer intern?

A Machine Learning Engineer Intern is a temporary, entry-level role where individuals work with data scientists and engineers to develop, test, and optimize machine learning models. Interns typically assist in data preprocessing, feature engineering, model training, and evaluation. They may also work on improving existing algorithms, implementing research papers, or deploying models into production. This role provides hands-on experience with machine learning frameworks such as TensorFlow and PyTorch, as well as coding in Python and working with large datasets. The internship helps build practical skills and industry experience in artificial intelligence and data science.

What do machine learning engineer interns do?

Machine Learning Engineer Interns are often involved in data preparation, feature engineering, model development, and performance evaluation under the guidance of senior engineers or data scientists. You may help implement and test machine learning algorithms, assist in cleaning and visualizing datasets, and contribute to code reviews or research tasks. Interns frequently collaborate with cross-functional teams, such as data scientists, software engineers, and product managers, to solve real-world problems and support ongoing projects. This hands-on experience provides valuable insights into the practical application of machine learning in a professional setting.

What skills and qualifications are needed to thrive as a machine learning engineer intern?

To thrive as a Machine Learning Engineer Intern, you need a solid understanding of programming languages such as Python, knowledge of machine learning algorithms, and experience with data analysis, typically supported by coursework in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is often required. Strong problem-solving abilities, attention to detail, and effective communication are valuable soft skills in this role. These competencies enable interns to contribute meaningfully to projects, collaborate efficiently with teams, and adapt in a fast-paced, tech-driven environment.

What are the most commonly searched types of Machine Learning Engineer jobs in Milwaukee, WI?

The most popular types of Machine Learning Engineer jobs in Milwaukee, WI are:

What are popular job titles related to Machine Learning Engineer Intern jobs in Milwaukee, WI?

For Machine Learning Engineer Intern jobs in Milwaukee, WI, the most frequently searched job titles are:

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The top searched job categories for Machine Learning Engineer Intern jobs in Milwaukee, WI are:

What cities near Milwaukee, WI are hiring for Machine Learning Engineer Intern jobs?

Cities near Milwaukee, WI with the most Machine Learning Engineer Intern job openings:

Machine Learning Engineer Intern jobs near you
Infographic showing various Machine Learning Engineer Intern job openings in Milwaukee, WI as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $41,919 per year, or $20.2 per hour.

Applied Machine Learning Engineer I - Advanced Engineering & Technology

Milwaukee Tool

Brookfield, WI • On-site

Other

Medical, Dental, Vision, Retirement

Re-posted 7 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.

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