Senior Machine Learning Operations Engineer

Hungryroot is an AI-powered food and wellness company that provides personalized groceries, recipes, and supplements. The Senior Machine Learning Operations Engineer will build and operate the production systems powering grocery recommendations and box personalization, including backend services, APIs, data pipelines, and model lifecycle infrastructure. The role will also improve system reliability, observability, performance, and engineering foundations while collaborating with data scientists, operations researchers, and product engineers.

Responsibilities:

• 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

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 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
• 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++