Machine Learning Engineer II

The Machine Learning Engineer II role focuses on designing, developing, and validating machine learning models for power tool solutions, collaborating with cross-functional teams to deploy ML features across Milwaukee products globally, and owning projects that require strong problem-solving, communication, and project-management skills. This onsite position is based in Brookfield, Wisconsin.

Responsibilities

• Design, implement, and validate machine learning models tailored to power tool applications.
• Collaborate with highly cross-functional teams to deliver solutions that enhance user outcomes.
• Explore and evaluate new machine learning approaches for deployment within Milwaukee products worldwide, demonstrating strong problem-solving, critical thinking, and composure in a dynamic environment.
• Communicate technical concepts effectively and exercise fundamental project management capabilities.
• Exhibit proactive ownership of projects and tasks, with an understanding of their links to broader initiatives.

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
• 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 involving embedded systems, edge computing, signal processing, or related areas.
• Proven experience applying fundamental machine learning algorithms in non-coursework settings, 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.
• Proficiency in Python and debugging, with extensive use of libraries such as NumPy, pandas, scikit-learn, and Matplotlib.
• Experience with at least one deep learning framework, such as PyTorch or TensorFlow.
• Strong mathematical foundation in statistics, linear algebra, calculus, and optimization.
• Experience using modern software development tools and version control systems.
• Excellent problem-solving abilities, critical thinking, and capability to work under pressure in a dynamic environment.
• Strong technical communication skills and fundamental project management abilities.
• Demonstrated ownership of a project or tasks and understanding of their relationships to other initiatives.
• Ability to travel up to 10 percent of the time, domestically and internationally.

Technologies

• Python
• NumPy
• pandas
• scikit-learn
• Matplotlib
• CNNs
• transformers
• computer vision
• PyTorch
• TensorFlow
• 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 a related field is preferred.
• Three or more years of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing, or related fields; an advanced degree may count toward part of this experience.
• Experience with time series modelling, including domains such as NLP, SLAM, forecasting, or audio/video processing.
• A proven track record of developing, deploying, and implementing AI or ML solutions aligned with business objectives.
• Proficiency in developing and debugging code in an embedded environment using C or C++.
• Working knowledge of sensor technologies (e.g., IMU, thermistors, magnetic and optical sensors) and interfacing to microcontrollers.
• Understanding of embedded systems architecture (hardware and software), including microcontroller design and operation.
• Experience with diverse data collection methods and their application in relevant environments.
• Experience developing and deploying machine learning algorithms to edge environments.
• Proven ability to develop robust MLOps pipelines ensuring efficient deployment, monitoring, and scaling of ML models.