Machine Learning Engineer II - Operations

Milwaukee Tool•Milwaukee, WI

1d•Onsite

About The Position

Milwaukee Tool is seeking a Machine Learning Engineer II to join their Operations team. This role focuses on designing, developing, and deploying machine learning solutions to enhance manufacturing and service operations. The engineer will collaborate with various teams, including operations, quality, supply chain, engineering, and service, to create data-driven solutions for global business and operational challenges. The position involves the full machine learning lifecycle, from data engineering and model development to deployment and monitoring on Azure and Databricks. A key responsibility is partnering with Global and Service Teams to deploy, validate, and support these solutions, ensuring they deliver measurable value. The ideal candidate is self-motivated, thrives in a fast-paced environment, communicates effectively across technical and non-technical audiences, and takes ownership of delivering production-ready solutions.

Requirements

Bachelor of Science Degree in Computer Science, Computer Engineering, Electrical Engineering or other scientific or engineering discipline.
Completed coursework 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).
Solid 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.
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).

Nice To Haves

Master’s degree or PhD in Machine Learning or 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, 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.

Responsibilities

Design, develop, and deploy machine learning solutions to improve manufacturing and service operations.
Develop and implement data-driven solutions addressing real-world business and operational challenges globally, working cross-functionally with operations, quality, supply chain, engineering, and service teams.
Contribute to the full machine learning lifecycle, including data engineering, model development, deployment, and monitoring on Azure and Databricks.
Partner with Global and Service Teams to deploy, validate, and support machine learning solutions in operational environments, ensuring models deliver measurable value.
Translate complex ML deployments into language understandable by non-technical audiences.
Collaborate with global teams, adjusting working hours to accommodate international time zones and ensure project alignment.
Travel up to 20% of the time (domestic and international).