Applied Machine Learning Engineer II - Advanced Engineering & Technology

Milwaukee Tool•Brookfield, WI

2d•Onsite

About The Position

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 research, prototype, and deliver ML-driven capabilities that accelerate how we design and develop products. You will take ideas from conceptual whiteboard architectures through functional prototypes and 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.

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.
+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).

Nice To Haves

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

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.