Sr Machine Learning Engineer

Milwaukee Tool•Milwaukee, WI

1d•Onsite

About The Position

At Milwaukee Tool, we believe our People and Culture are key to our success. We provide unlimited access to resources for developing disruptive new technologies and solutions across our operations teams. Our Operations Teams are responsible for manufacturing, supply chain, and quality systems. We are investing in advanced analytics, machine learning, and AI to transform our plants, optimize production, and anticipate issues. We are pushing the limits in data engineering, deep learning, and generative AI for real-world manufacturing problems. As a Senior Machine Learning Engineer, you will design, develop, and deploy intelligence solutions to transform product manufacturing, including predictive quality, yield optimization, demand forecasting, anomaly detection, and intelligent automation across plants and distribution networks. You will collaborate with operations, quality, supply chain, and plant leadership to identify opportunities, then lead data engineering and modeling end-to-end: from sourcing sensor and MES data, to training and validating models, to deploying and monitoring them in production on Azure and Databricks. This role requires an independent self-starter who can lead strategic change, manage projects across multiple sites and stakeholders, and operate with ownership in a fast-moving manufacturing environment.

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 using one or more deep learning frameworks (PyTorch, TensorFlow, Keras, etc).
Proficiency in big data transformation using Spark, SQL and Python (NumPy, pandas, scikit-learn, Matplotlib).
Solid mathematical foundation in statistics, linear algebra, calculus, and optimization.
Five years of hands-on experience applying machine learning, deep learning, and AI techniques to dynamic, real-world manufacturing problems.
Demonstrated success deploying ML and AI solutions using CI/CD to cloud services (Azure, DataBricks, MLFlow) and edge devices (GPU, Containerization, Linux).
Excellent problem solving and technical communication skills translating complex solutions into decisions and actions for plant, quality, and supply chain stakeholders.
Demonstrated ability to work as an independent self-starter and lead strategic change — identifying opportunities, building alignment, and driving adoption of new data and AI capabilities across operations teams.
Experience collaborating with global teams, including a willingness to adjust working hours to accommodate international time zones and ensure project alignment.

Nice To Haves

Master’s degree in Machine Learning, Data Science, Computer Science, or a related quantitative field is preferred.
Seven or more years of hands-on experience delivering machine learning and AI solutions in manufacturing, supply chain, quality, or other industrial operations domains (an advanced degree may count toward some experience).
Experience with time-series modeling for use cases such as demand forecasting, predictive maintenance, yield prediction, or process anomaly detection.
Proven track record of developing, deploying, and scaling AI or ML solutions tied to measurable operations outcomes (e.g., scrap reduction, throughput, OEE, on-time delivery, inventory turns).
Desktop application development experience (e.g., building tools or UIs that put models in the hands of plant and operations users).
Hands-on data engineering experience building pipelines on Databricks / Spark against large operational datasets (MES, ERP, SCADA, IoT / sensor telemetry).
Experience applying generative AI or LLMs to operations problems such as knowledge retrieval, document processing, or assistive tooling for plant teams.
Demonstrated ability to develop robust MLOps pipelines and ensure efficient deployment, monitoring, and scaling of ML models in production.

Responsibilities

Design, develop, and deploy intelligence solutions that transform how Milwaukee Tool manufactures product.
Lead data engineering and modeling work end-to-end: from sourcing sensor and MES data, to training and validating models, to deploying and monitoring them in production on Azure and Databricks.
Partner with operations, quality, supply chain, and plant leadership to identify high-impact opportunities.
Lead strategic change, manage projects across multiple sites and stakeholders, and operate with a strong sense of ownership.
Adjust working hours to accommodate international time zones and ensure project alignment.
Travel up to 10% of the time (domestic and international).