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Machine Learning Engineer Associate Jobs in Milwaukee, WI

Senior MLOps Engineer (Remote)

Menomonee Falls, WI · On-site

$104K - $144K/yr

Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including ... developing reusable frameworks and standardized solutions to streamline model implementation

Senior Forward Deployed Engineer- AWS

Milwaukee, WI · On-site

$103K - $141K/yr

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ years of experience building reliable, maintainable, and well-documented code * Ability to travel 50 ...

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ years of experience building reliable, maintainable, and well-documented code * Ability to travel 50 ...

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ years of experience building reliable, maintainable, and well-documented code * Ability to travel 50 ...

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

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 ...

Focused on relationships, you are building meaningful client connections, and learning how to ... Engineer Associate, Snowflake Core, Snowflake Databricks Data Engineer Associate] is a plus ...

Showing results 21-40

Machine Learning Engineer Associate information

See Milwaukee, WI salary details

$40.9K

$81.4K

$130.1K

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

As of Aug 10, 2026, the average yearly pay for machine learning engineer associate in Milwaukee, WI is $81,417.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,500.00 and $93,600.00 per year, depending on experience, location, and employer.

What are some common challenges faced by machine learning engineer associates when deploying models to production?

Machine Learning Engineer Associates often encounter challenges such as ensuring model scalability, managing data pipeline reliability, and addressing issues with model drift after deployment. Collaborating closely with data engineers and software developers is essential to integrate models seamlessly into existing systems. Additionally, balancing model performance with resource constraints and maintaining clear documentation for reproducibility are important aspects of the role. Gaining familiarity with deployment tools and best practices can help overcome these hurdles.

What is a machine learning engineer associate?

Machine Learning Engineer Associates are entry-level professionals who help design, build, and maintain machine learning models and systems. They typically work under the guidance of senior engineers, assisting in data preprocessing, model training, and testing. Their responsibilities may include implementing algorithms, evaluating model performance, and deploying solutions to production environments. This role requires a strong foundation in programming, statistics, and machine learning principles, often acquired through education or internships.

What are the key skills and qualifications needed to thrive as a machine learning engineer associate, and why are they important?

To thrive as a Machine Learning Engineer Associate, you need a solid understanding of programming (especially Python), mathematics, and foundational machine learning concepts, typically supported by a relevant degree or coursework. Familiarity with tools and frameworks like TensorFlow, PyTorch, scikit-learn, and experience with version control systems such as Git are essential. Strong problem-solving abilities, communication skills, and a collaborative mindset help you work effectively within technical teams. These competencies ensure you can develop, implement, and improve machine learning models that deliver actionable insights and drive business value.
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 Associate jobs in Milwaukee, WI? For Machine Learning Engineer Associate jobs in Milwaukee, WI, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Associate jobs in Milwaukee, WI look for? The top searched job categories for Machine Learning Engineer Associate jobs in Milwaukee, WI are:
What cities near Milwaukee, WI are hiring for Machine Learning Engineer Associate jobs? Cities near Milwaukee, WI with the most Machine Learning Engineer Associate job openings:
Infographic showing various Machine Learning Engineer Associate job openings in Milwaukee, WI as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, 1% Temporary, and 2% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $81,417 per year, or $39.1 per hour.

Applied Machine Learning Engineer II - Advanced Engineering & Technology

Milwaukee Tool

Brookfield, WI • On-site

Full-time

Re-posted 4 days ago


Job description

Job Summary:
Milwaukee Tool is a company that values innovation and culture, seeking to create disruptive technologies and solutions. The Applied Machine Learning Engineer II will utilize their machine learning expertise to deliver innovative technologies and accelerate product development in the Power Tool Accessories business unit.
Responsibilities:
• 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, evaluate, and deploy ML models to solve applied science and engineering problems that expand product development capabilities.
• Build end‑to‑end ML workflows spanning data acquisition, feature engineering, model development, validation, and deployment (PyTorch, TensorFlow, CUDA, Azure ML).
• 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.
• Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs.
• Identify and assess emerging technologies via literature, universities, conferences, and vendor engagement.
Qualifications:
Required:
• 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.
• +3 or more years of experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar).
• Demonstrated experience designing, training, evaluating, and deploying ML models on real-world problems.
• Strong working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikit‑learn), with working knowledge of SQL.
• Hands-on experience with at least one deep learning framework (PyTorch or TensorFlow) and familiarity with 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.
• Demonstrated ability to formulate ambiguous engineering or scientific problems into well-defined ML problems with clear objectives and evaluation criteria.
• 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 or PhD in relevant field.
• Familiarity with physics-informed ML approaches, embedding physical constraints in model architecture, or surrogate modeling for simulation acceleration.
• Experience with computer vision for engineering applications.
• Exposure to edge deployment: model optimization containerized deployment to industrial hardware.
• Experience with design of experiments (DOE), uncertainty quantification, or Bayesian optimization.
• Familiarity with version control, experiment tracking, and reproducible research practices.
Company:
Milwaukee Tool manufactures electric power tools and accessories. It is a sub-organization of Techtronic Industries. Founded in 1924, the company is headquartered in Brookfield, USA, with a team of 5001-10000 employees. The company is currently .