About the role

Machine Learning Engineer II developing machine learning solutions for supply chain optimization at Grainger. Collaborating with data scientists and engineers to build robust data pipelines and ML models.

Responsibilities

Partner with data scientists and data engineers to develop, deploy, and maintain machine learning solutions, from data pipelines to production model serving.
Build scalable, efficient, and automated processes for large-scale data analysis, model development, validation, and deployment.
Design and maintain ETL pipelines and workflow orchestration to support production ML systems.
Deploy and operate machine learning workloads and services on containerized infrastructure (AWS, Kubernetes).
Automate critical system operations and improve reliability, observability, and performance of ML systems.
Explore and evaluate emerging technologies and tools to improve ML development velocity and platform capabilities.
Provide technical support to platform users throughout the ML development lifecycle and assist in resolving production issues.
Develop documentation and best practices to help users more effectively leverage ML systems and tools.

Master’s degree in computer science, data science, analytics, or a related technical field required.
2+ years of experience developing, deploying, and maintaining production machine learning or data-intensive software systems using Python.
Strong software engineering fundamentals, including version control, testing, and CI/CD practices.
Experience working with containerized environments (Docker, Kubernetes).
Experience deploying or supporting machine learning models in production, including batch and/or real-time inference.
Familiarity with AWS services such as S3, ECR, Secrets Manager, or similar cloud platforms.
Experience building data pipelines and automating workflows using orchestration tools (e.g., Airflow, Astronomer).
Working knowledge of databases and data querying (e.g., SQL, Snowflake, DuckDB).
Understanding of core machine learning concepts and the model development lifecycle, including time series forecasting, clustering, and operations research–based optimization models (e.g., Gurobi, Pyomo).
Strong communication and collaboration skills, with the ability to work effectively across engineering and data science teams.
Self-directed, curious, and motivated to learn and apply new technologies.

Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing.
18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date) and 6 company holidays per year.
6% company contribution to a 401(k) Retirement Savings Plan each pay period, no employee contribution required.
Employee discounts, tuition reimbursement, student loan refinancing and free access to financial counseling, education, and tools.
Maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non-birth parents.