Machine Learning Engineer II

Milwaukee Electric Tool Corporation seeks a Machine Learning Engineer II to create, develop, and validate machine learning models for power tool solutions, collaborating with cross-functional teams to deploy ML across Milwaukee products worldwide. The role emphasizes ownership, strong problem-solving and communication skills, and project-management capabilities. This onsite position is based in Brookfield, WI, and requires at least one year of hands-on ML experience along with a bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.

Responsibilities

• Create, develop, and validate machine learning models.
• Work with highly cross-functional teams to make power tool solutions that change the lives of our users.
• Innovate and explore new machine learning solutions to deploy into Milwaukee products around the world while demonstrating excellent problem-solving skills, critical thinking, and the ability to thrive under pressure in a dynamic environment.
• Success in this role also requires strong technical communication skills and fundamental project management abilities.
• Demonstrate a proactive sense of ownership for projects and tasks and an understanding of how they connect to broader initiatives.

Requirements

• Completed course work or specialization in Machine Learning and/or Data Science
• At least one year of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing or a related field
• Demonstrated experience applying fundamental machine learning algorithms and techniques in a non-coursework setting (e.g. unsupervised or supervised learning, classification/regression, dimensionality reduction, model optimization)
• Demonstrated experience with machine learning and AI methods such as CNNS, transformers, or computer vision
• Proficient developing and debugging code in Python
• Proficiency in Python, with extensive experience in common libraries (NumPy, pandas, scikit-learn, Matplotlib, etc.)
• Proficiency with at least one deep learning framework (e.g. PyTorch or Tensor Flow)
• Solid mathematical foundation in statistics, linear algebra, calculus and optimization
• Experience working with modern software development tools and version control tools
• Excellent problem-solving skills, critical thinking, and ability to work well under pressure in a dynamic environment
• Excellent technical communication skills and fundamental project management abilities
• Demonstrated strong sense of ownership of a project or tasks and understanding of relationships to other tasks/projects
• Ability to travel up to 10% of the time (domestic and international)

Technologies

• Python
• NumPy
• pandas
• scikit-learn
• Matplotlib
• CNNS
• transformers
• computer vision
• PyTorch
• Tensor Flow
• C
• C++

Benefits

• Health, dental and vision insurance
• 401(k) savings plan
• Education assistance
• On-site wellness, fitness center, food, and coffee service

Other Tools We Prefer You to Have

• Master’s degree or PhD in Machine Learning or related field is preferred
• At least three years of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing or a related field (an advanced degree may count toward some experience)
• Experience with time series modelling, especially with related domains such as NLP, SLAM, forecasting, or audio/video processing
• Proven track record of developing, deploying and implementing AI or ML solutions connected to business objectives
• Proficient developing and debugging code in an embedded environment in a programming language such as C or C++
• Working knowledge of various sensor technologies (e.g. IMU, thermistors, magnetic and optical) and interfacing to microcontrollers
• Working knowledge of embedded systems architecture (HW & SW), microcontroller design and operation
• Experience with different types of data collection methods, understanding their principles and demonstrating their value in relevant environments
• Experience developing and deploying machine learning algorithms to edge environments
• Demonstrated ability to develop robust MLOps pipelines and ensure efficient deployment, monitoring and scaling of ML models