Machine Learning Engineer II

Job Description:

Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position at this time.

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 on our engineering teams. Our Engineering Team is responsible for giving life to the batteries, motors, and electronics that power solutions changing the lives of our users. Every developmental phase of these critical components happens in-house under the watch of this team. We continue to invest in engineering resources to design and develop leadership in electronic capabilities; something unique within the industry. And we’re pushing the limits in firmware engineering, power electronics, embedded systems, machine learning, and the use of artificial intelligence.

Your role on our team

As a Machine Learning Engineer II, you will create, develop, and validate machine learning models while working with highly cross-functional teams to make power tool solutions that change the lives of our users. You will 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, along with a proactive sense of ownership for projects and tasks and an understanding of how they connect to broader initiatives.

What TOOLS you’ll bring with you:

• Bachelor of Science Degree in Computer Science, Computer Engineering, Electrical Engineering or other scientific or engineering discipline.
• 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 of Tensor Flow)
• Sold 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).

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

We provide these great perks and benefits:

• Robust health, dental and vis