Machine Learning Engineer II - Operations

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 drive disruptive new technologies and solutions across our operations teams. Our Operations Teams are responsible for the manufacturing, service, supply chain, and quality systems that bring our products to life and into the hands of our users. We continue to invest in advanced analytics, machine learning, and AI capabilities to transform how we run our plants, optimize production, and anticipate issues before they reach the line. We’re pushing the limits in data engineering, deep learning, and generative AI applied to real-world manufacturing problems.

Your role on our team:

As a Machine Learning Engineer II, you will design, develop, and deploy machine learning solutions that improve how Milwaukee Tool manufactures and services products. Working cross-functionally with operations, quality, supply chain, engineering, and service teams, you will develop and implement data-driven solutions that address real-world business and operational challenges globally.

You will contribute to the full machine learning lifecycle, from data engineering and model development to deployment and monitoring on Azure and Databricks. A key aspect of this role is partnering with our Global and Service Teams to deploy, validate, and support machine learning solutions in operational environments, ensuring models deliver measurable value where they are used.

Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position at this time., This role is ideal for a self-motivated engineer who thrives in a fast-paced environment, communicates effectively across technical and non-technical teams, and takes ownership of delivering impactful, production-ready solutions.

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 using one or more deep learning frameworks (PyTorch, TensorFlow, Keras, etc).

• At least one year of hands-on experience applying machine learning principles and algorithms to dynamic, real-world problems.

• 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.

• Proficiency in big data transformation using Spark, SQL, and Python (NumPy, pandas, scikit-learn, Matplotlib).

• Sold mathematical foundation in statistics, linear algebra, calculus and optimization.

• Experience working with ML deployments using CI/CD pipelines (Azure, Databricks, MLFlow) and edge devices (GPU, Containerization, Linux).

• Excellent problem-solving and technical communication skills translating complex ML deployments into language that non-technical audience can understand.

• Experience collaborating with global teams, including a willingness to adjust working hours to accommodate international time zones and ensure project alignment.

Ability to travel up to 20% 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 to dynamic, real-world problems.

• Experience with time-series modeling for use cases such as demand forecasting, predictive maintenance, yield prediction, or process anomaly detection.

• Experience with computer vision for use cases such as defect detection, missing part detection, part quality inspection, part counting, etc.

• 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 or Web app 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.

• Experience in developing, maintaining and using MLOps pipelines and ensure efficient deployment, monitoring, and scaling of ML models in production.

Experience developing and deploying machine learning algorithms to edge environments. *