📋 Description

• Design, build, and operate scalable backend services, APIs, and data pipelines.
• Improve the reliability, performance, and observability of production ML and optimization systems.
• Own the path from trained model to production: model versioning and registry (MLflow), safe rollout
• Build clean interfaces that let new ML models and decisioning capabilities integrate safely and
• Strengthen engineering foundations across a growing codebase: automated testing, type checking
• Profile data-heavy services and pipelines; reduce execution time and memory footprint where it

🎯 Requirements

• 5+ years in MLOps, ML engineering, or DevOps with a focus on production ML infrastructure.
• Strong Python and SQL; Bash for automation and tooling.
• Experience designing and operating backend services and APIs (e.g., FastAPI) with attention to
• Hands-on experience with Databricks and Spark (jobs/workflows, Unity Catalog a plus) and MLflow or
• Experience building CI/CD for ML or data systems (Git, GitHub Actions/Jenkins, Databricks Asset
• Solid AWS fundamentals: IAM, networking, compute/cluster management, containerized workloads

🎁 Benefits

• Remote-first: work from home, work from our NYC office, work from anywhere in the U.S. - you decide!
• Equity
• Unlimited vacation policy
• Universal paid parental leave
• Monthly Hungryroot credit for delicious, healthy groceries
• Comprehensive health, vision, dental, and life insurance