Machine Learning Engineer II - Operations

Milwaukee Electric Tool Corporation is looking for a Machine Learning Engineer II to help design, build, and deploy machine learning solutions that improve how Milwaukee Tool manufactures and services products. This onsite role in Milwaukee supports end-to-end work across the machine learning lifecycle, pairing data engineering with model development, deployment, and ongoing monitoring.

You will collaborate with operations-related teams worldwide to deliver data-driven solutions in real operational environments, using Azure and Databricks as core platforms.

• Design, develop, and deploy machine learning solutions that improve manufacturing and service workflows.
• Partner cross-functionally with operations, quality, supply chain, engineering, and service teams to address real business and operational challenges on a global scale.
• Deliver full lifecycle machine learning work, from data engineering and model development through deployment and monitoring on Azure and Databricks
  .
• Support Global and Service Teams by deploying, validating, and maintaining machine learning solutions in operational environments, ensuring models create measurable value where used.
• Develop and implement data-driven solutions in operational environments globally.

• Bachelor of Science degree in Computer Science, Computer Engineering, Electrical Engineering, or another scientific or engineering discipline.
• Completed coursework or specialization in Machine Learning and/or Data Science using deep learning frameworks such as Py Torch ,
  Tensor Flow
  , or Keras
  .
• 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 outside coursework, including areas such as unsupervised or supervised learning, classification/regression, dimensionality reduction, and 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 (Num Py, pandas, scikit-learn, Matplotlib).
• Strong mathematical foundation in statistics, linear algebra, calculus, and optimization.
• Experience with ML deployments using CI/CD pipelines (Azure, Databricks, MLFlow) and edge environments (GPU, containerization, Linux).
• Excellent problem-solving and technical communication skills to explain complex ML deployments to non-technical audiences.
• Experience collaborating with global teams, including willingness to adjust working hours to match international time zones and align on delivery.

• PyTorch, Tensor Flow, Keras
• Spark, SQL, Python, Num Py, pandas, scikit-learn, Matplotlib
• Azure, Databricks, MLFlow
• CI/CD, GPU, containerization, Linux
• CNNs, transformers

• Master’s degree or PhD in Machine Learning or a related field.
• 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, and related applications.
• Proven track record of developing, deploying, and scaling AI or ML solutions tied to measurable operations outcomes (examples include scrap reduction, throughput, OEE, on-time delivery, or inventory turns).
• Desktop or web application development experience (for building tools or UIs that help plant and operations users use models).
• Hands-on data engineering experience building pipelines on Databricks/Spark against large operational datasets such as MES, ERP, SCADA, or 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 developing, maintaining, and using MLOps pipelines to support efficient deployment, monitoring, and scaling.
• Experience developing and deploying machine learning algorithms to edge environments.

• Robust health, dental, and vision insurance plans.
• Generous 401(K) savings plan.
• Education assistance.
• On-site wellness, fitness center, food, and coffee service.