Machine Learning Engineer II

Responsibilities

• Create, advance, and validate machine learning models.
• Collaborate with diverse cross-functional teams to deliver power tool solutions that impact users.
• Explore and deploy new machine learning approaches within Milwaukee products worldwide, while demonstrating strong problem-solving, critical thinking, and the ability to thrive under pressure in a dynamic environment.
• Maintain strong technical communication and foundational project management capabilities.
• Show initiative and ownership for projects and tasks, understanding how they connect to broader initiatives.

Requirements

• Completed coursework or specialization in machine learning and/or data science.
• At least one year of hands-on experience applying machine learning principles and algorithms in contexts such as embedded systems, edge computing, or signal processing.
• Proven experience applying core machine learning algorithms and techniques outside coursework, including unsupervised or supervised learning, classification/regression, dimensionality reduction, and model optimization.
• Experience with machine learning and AI methods such as CNNS, transformers, or computer vision.
• Proficient in developing and debugging code in Python.
• Strong Python proficiency with extensive experience using libraries such as NumPy, pandas, scikit-learn, and Matplotlib.
• Experience with at least one deep learning framework (e.g., PyTorch or Tensor Flow).
• Solid foundation in statistics, linear algebra, calculus, and optimization.
• Experience with modern software development workflows and version control tools.
• Excellent problem-solving and critical-thinking abilities, with the ability to work well under pressure in a dynamic environment.
• Strong technical communication skills and foundational project management abilities.
• Proven ownership of projects or tasks and understanding of their relationships to other work.
• Ability to travel up to 10 percent 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