Drive scalable, production-grade machine learning and operations research solutions in a hybrid role in Chicago.

Lead the development of scalable automated workflows for large-scale data analysis, model training, validation, and deployment.
Oversee implementation and continuous improvement of machine learning and operations research models in collaboration with data scientists.
Partner cross-functionally to ensure model and analytics efforts align with business needs.
Design and maintain robust ETL pipelines and automate critical system operations for reliable data flow and model performance.
Build internal tools and web applications enabling stakeholders to interact with ML and ERP systems.
Evaluate and integrate emerging technologies to support innovation and strategic alignment.
Ensure production-grade reliability and maintainability of deployed ML solutions using software engineering and DevOps best practices.
Mentor and guide team members on technical execution, architecture decisions, and career development.
Create and maintain architectural diagrams and documentation to support system design, scalability, and knowledge sharing.

Bachelor’s degree in Computer Science, Engineering, or a related field (required).
Master’s degree in a related technical field or equivalent practical experience (preferred).
3+ years production ML, optimization, modern data tools, and scalable engineering practices (required).
Demonstrated experience with AWS services including S3, ECR, and Secrets Manager.
Ability to deploy and maintain production-grade machine learning solutions using Python.
Apply mathematical optimization using solvers such as Gurobi.
Develop and support web applications integrated with machine learning and ERP systems.
Apply knowledge of operations research and optimization modeling.
Use database technologies such as Snowflake and DuckDB with SQL for data querying.
Work with containerization tools including Kubernetes and Docker.
Understand machine learning concepts including time series forecasting and clustering.
Follow software engineering best practices including version control and CI/CD (for example, GitHub, GitHub Actions).
Automate workflows using orchestration tools such as Astronomer or Airflow.
Build and support solutions within the Databricks platform.
Use machine learning frameworks and libraries such as PyTorch and scikit-learn.

Medical, dental, vision, and life insurance plans with coverage starting on day one
6 free sessions each year with a licensed therapist
18 paid time off (PTO) days annually for full-time employees
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
Free access to financial counseling, education, and tools
Maternity support programs
Nursing benefits
Up to 14 weeks paid leave for birth parents
Up to 4 weeks paid leave for non-birth parents

Anticipated base pay range: $134,100.00 – $223,500.00 (per year)
Eligible for an incentive target of up to 10% based on individual and company performance objectives (terms subject to change)
Not eligible for visa sponsorship

Design and deliver scalable, production-grade solutions that enhance data-driven decision-making across the enterprise.
Lead development of robust automated data pipelines, deploy advanced ML and operations research models, and create reusable tools and frameworks to accelerate experimentation and model delivery.
Collaborate with cross-functional teams including planning, product management, and analytics to improve availability, efficiency, and overall business performance across operational domains.

Senior Machine Learning Engineer

Job Description