Senior Machine Learning Operations Engineer

• 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 and rollback, and monitoring for data quality and model drift.
• Build clean interfaces that let new ML models and decisioning capabilities integrate safely and efficiently, including experimentation and feature-flag tooling.
• Strengthen engineering foundations across a growing codebase: automated testing, type checking, CI/CD, infrastructure as code, documentation, and thoughtful system design.
• Profile data-heavy services and pipelines; reduce execution time and memory footprint where it matters.
• Collaborate with data scientists, operations researchers, and product engineers to translate business needs into robust technical solutions.

• 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 reliability, latency, and scalability.
• Hands-on experience with Databricks and Spark (jobs/workflows, Unity Catalog a plus) and MLflow or comparable model lifecycle tooling (registry, versioning, experiment tracking).
• Experience building CI/CD for ML or data systems (Git, GitHub Actions/Jenkins, Databricks Asset Bundles) and infrastructure as code (Terraform or similar).
• Solid AWS fundamentals: IAM, networking, compute/cluster management, containerized workloads (Docker; ECS or EKS).
• Experience with production observability: metrics, logging, alerting, and ML-specific monitoring like data quality and model drift

Nice to Haves

• Familiarity with recommendation, personalization, or operations research systems — especially productionizing them.
• Experience with optimization solvers and OR tooling (e.g., Gurobi, OR-Tools) alongside data science or operations research teams.
• Experience integrating experimentation and feature-flag platforms (e.g., Statsig) into production ML services and data pipelines, ideally with warehouse-native setups on Databricks.
• Feature store experience (Databricks Feature Store, Feast, Tecton) serving consistent online/offline features.
• Experience with low-latency model serving and deployment patterns (canary, blue/green, shadow).
• Experience optimizing cost and performance of data-heavy workloads (Spark tuning, cluster right-sizing).
• Additional languages such as Scala or C++.

• 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
• 401k with Company Match
• A work from home stipend to support your initial home-office setup

Expected Pay Range $170,000 - $210,000

#LI-REMOTE

The employer will not sponsor applicants for work visas.

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We do not discriminate on the basis of any protected class.