Machine Learning Engineer II

Milwaukee Tool•Brookfield, WI

13d•Onsite

About The Position

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.

Requirements

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).

Nice To Haves

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

Responsibilities

Create, develop, and validate machine learning models.
Work with highly cross-functional teams.
Innovate and explore new machine learning solutions to deploy into Milwaukee products.
Demonstrate excellent problem-solving skills, critical thinking, and the ability to thrive under pressure in a dynamic environment.
Utilize strong technical communication skills and fundamental project management abilities.
Take a proactive sense of ownership for projects and tasks and understand how they connect to broader initiatives.