Machine Learning Engineer II

Grainger Businesses•Chicago, IL

22h•Hybrid

About The Position

W.W. Grainger, Inc., is a leading broad line distributor with operations primarily in North America, Japan and the United Kingdom. At Grainger, We Keep the World Working® by serving more than 4.5 million customers worldwide with products and solutions delivered through innovative technology and deep customer relationships. Known for its commitment to service and award-winning culture, the Company had 2024 revenue of $17.2 billion across its two business models. In the High-Touch Solutions segment, Grainger offers approximately 2 million maintenance, repair and operating (MRO) products and services, including technical support and inventory management. In the Endless Assortment segment, Zoro.com offers customers access to more than 14 million products, and MonotaRO.com offers more than 24 million products. For more information, visit www.grainger.com. Grainger’s Inventory Planning and Optimization organization manages over 10 million SKUs and nearly $2 billion in inventory across distribution centers and branches in North America. We are hiring a Machine Learning Engineer II to support and develop machine learning solutions that enable data scientists and supply chain stakeholders to make analytics-driven decisions on where, when, and how much inventory is needed to best serve customers. In this role, you will work closely with data scientists, product managers, and data engineers to build, deploy, and operate production machine learning systems, with a focus on scalable data pipelines, model deployment, and operational reliability. You will help modernize our ML tooling and infrastructure while enabling faster experimentation and delivery of business impact. You will report to the Sr. Manager, Machine Learning Engineering – Supply Chain Optimization. This position is located at the Merchandise Mart in downtown Chicago, IL working hybrid 2-3 days per week.

Requirements

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.

Nice To Haves

Experience with MLOps tooling (e.g., MLflow, Kubeflow).
Experience with Databricks for scalable data processing and machine learning workflows.
Experience applying optimization solvers (e.g., Gurobi or equivalent) to solve constrained planning and allocation problems.
Familiarity with infrastructure-as-code tools (e.g., Terraform).
Experience building internal tools or lightweight web applications to support analytics or ML workflows.

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.

Benefits

With benefits starting on day one, our programs provide choice and flexibility to meet team members' individual needs, including:
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.

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