Machine Learning Engineer II

What you'll work on

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.

You Will

• 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.

You Have

• 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.

What we're looking for

• 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.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, protected veteran status or any other protected characteristic under federal, state, or local law. We are proud to be an equal opportunity workplace.

We are committed to fostering an inclusive, accessible work environment that includes both providing reasonable accommodations to individuals with disabilities during the application and hiring process as well as throughout the course of one’s employment, should you need a reasonable accommodation during the application and selection process, including, but not limited to use of our website, any part of the application, interview or hiring process, please advise us so that we can provide appropriate assistance.

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