Applied Machine Learning Engineer I - Advanced Engineering & Technology

Milwaukee Tool•Brookfield, WI

13d•Onsite

About The Position

Milwaukee Tool is seeking an Applied Machine Learning Engineer I to join their Advanced Engineering and Technology (AET) Team within the Power Tool Accessories business unit. This role focuses on utilizing machine learning expertise to solve novel problems and develop first-of-its-kind technologies. The engineer will support research, prototyping, and delivery of ML-driven capabilities to enhance product design and development processes. Responsibilities include taking ideas from concept to functional prototypes and supporting integration with product and production engineering teams. This individual contributor role emphasizes applied execution and technology demonstration, working under shared technical direction. The position offers the opportunity for full-stack ML work in a physical domain, applying ML across Technology Readiness Levels (TRL 1-7), and flexibility in choosing tools and frameworks. The goal is to deliver ML-driven capabilities that shorten product development cycles and enable new engineering possibilities.

Requirements

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).

Nice To Haves

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.

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, 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.