Machine Learning Engineer II - Operations

DataJobs; Milwaukee (WI); On-site; USD 100,000 - 150,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Education assistance

On-site wellness

Job summary

Milwaukee Electric Tool Corporation is seeking a Machine Learning Engineer II to design, build, and deploy ML solutions that enhance manufacturing and service workflows. This onsite role in Milwaukee collaborates with global teams and leverages Azure and Databricks across the ML lifecycle.

The candidate will work on data engineering and model development, deployment, and ongoing monitoring, ensuring measurable value from ML initiatives in real operational environments.

Qualifications

• Bachelor's degree in CS/CE/EE or related field.
• Coursework or specialization in ML/DS using frameworks like PyTorch, TensorFlow, or Keras.
• 1+ year of hands-on ML experience solving real-world problems.
• Experience with ML algorithms beyond coursework (supervised/unsupervised, regression, classification).
• Experience with ML methods such as CNNs, transformers, or computer vision.
• Proficiency in big data transformation using Spark, SQL, and Python (NumPy, pandas, scikit-learn).
• Strong foundation in statistics, linear algebra, calculus, and optimization.
• Experience with ML deployments using CI/CD (Azure, Databricks, MLFlow) and edge environments.
• Ability to communicate complex ML deployments to non-technical audiences.
• Experience collaborating with global teams across time zones.

Responsibilities

• Design, develop, and deploy ML solutions that improve manufacturing and service workflows.
• Partner with operations, quality, supply chain, engineering, and service teams to address challenges globally.
• Deliver full lifecycle ML work from data engineering to deployment and monitoring on Azure and Databricks.
• Support global teams by deploying, validating, and maintaining ML solutions in production environments.
• Develop data-driven solutions in operational environments worldwide.

Skills

PyTorch

TensorFlow

Keras

Spark

SQL

Python

NumPy

pandas

scikit-learn

CNNs

Transformers

CI/CD pipelines

Azure

Databricks

MLFlow

GPU

Containerization

Linux

Education

Bachelor of Science in Computer Science, Computer Engineering, Electrical Engineering, or related

Master’s degree in Machine Learning or related field

PhD in Machine Learning or related field

Tools

Spark

SQL

Python

Job description

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.

What you’ll do

• 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.

Minimum qualifications

• 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, 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 (NumPy, 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.

Technologies you’ll work with

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

Preferred

• 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.

Benefits

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