Machine Learning Engineer II - Operations

Milwaukee Electric Tool Corporation is seeking a Machine Learning Engineer II for onsite work in Milwaukee, WI to design, develop, and deploy ML solutions across manufacturing and service operations.

Responsibilities

• Design, develop, and deploy machine learning solutions that improve how Milwaukee Tool manufactures and services products
• Collaborate cross-functionally with operations, quality, supply chain, engineering, and service teams to deliver data-driven solutions for real-world business and operational challenges globally
• Own end-to-end ML lifecycle work from data engineering and model development through deployment and monitoring
• Build and support operational deployments alongside Global and Service Teams, validating performance and ensuring models deliver measurable value where used
• Develop and implement data-driven solutions in operational environments across global sites

Requirements

• 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 PyTorch, TensorFlow, Keras, etc.
• At least 1 year of hands-on experience applying machine learning principles and algorithms to dynamic, real-world problems
• Demonstrated experience applying fundamental ML algorithms and techniques outside coursework (for example: unsupervised or supervised learning, classification/regression, dimensionality reduction, model optimization)
• Demonstrated experience with ML/AI methods such as CNNs, transformers, or computer vision
• Proficiency in big data transformation using Spark, SQL, and Python (including NumPy, pandas, scikit-learn, Matplotlib)
• Strong mathematical foundation in statistics, linear algebra, calculus, and optimization
• Experience deploying ML using CI/CD pipelines including Azure, Databricks, and MLFlow, plus edge devices (GPU, containerization, Linux)
• Excellent problem-solving and technical communication skills to translate complex ML deployments for non-technical audiences
• Experience collaborating with global teams, including flexibility to adjust working hours to accommodate international time zones

Preferred

• Master’s degree or PhD in Machine Learning or a related field
• At least 3 years of hands-on experience applying machine learning principles and algorithms to dynamic, real-world problems
• Experience with time-series modeling for scenarios such as demand forecasting, predictive maintenance, yield prediction, or process anomaly detection
• Experience with computer vision for defect detection, missing part detection, part quality inspection, part counting, and similar use cases
• Proven track record of developing, deploying, and scaling AI/ML solutions tied to measurable operations outcomes (for example: scrap reduction, throughput, OEE, on-time delivery, inventory turns)
• Desktop application or web app development experience (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 developing, maintaining, and using MLOps pipelines to enable efficient deployment, monitoring, and scaling
• Experience developing and deploying machine learning algorithms to edge environments

Technology Stack

• PyTorch, TensorFlow, Keras
• Spark, SQL, Python, NumPy, pandas, scikit-learn, Matplotlib
• Azure, Databricks, MLFlow
• CI/CD, GPU, containerization, Linux
• CNNs, transformers

Benefits

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