Senior Machine Learning Operations Engineer

About the Company

Hungryroot is using AI to build the most consumer-centric food and wellness company to ever exist. We act as your personal assistant for healthy living—getting to know your goals, lifestyle, and budget, and recommending and delivering healthy groceries, easy recipes, and essential supplements for you and your family.

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.
• Strengthen engineering foundations across a growing codebase: automated testing, type checking, CI/CD, infrastructure as code, and documentation.
• Profile data-heavy services and pipelines to reduce execution time and memory footprint.
• Collaborate with data scientists, operations researchers, and product engineers to translate business needs into 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).
• Hands-on experience with Databricks and Spark.
• Experience with MLflow or comparable model lifecycle tooling.
• Experience building CI/CD for ML or data systems (Git, GitHub Actions/Jenkins, Databricks Asset Bundles).
• Experience with infrastructure as code (Terraform or similar).
• Solid AWS fundamentals: IAM, networking, compute/cluster management, and containerized workloads (Docker, ECS, or EKS).
• Experience with production observability: metrics, logging, alerting, and ML-specific monitoring.

Preferred Qualifications

• Familiarity with recommendation, personalization, or operations research systems.
• Experience with optimization solvers and OR tooling (e.g., Gurobi, OR-Tools).
• Experience integrating experimentation and feature-flag platforms (e.g., Statsig).
• Feature store experience (Databricks Feature Store, Feast, Tecton).
• Experience with low-latency model serving and deployment patterns (canary, blue/green, shadow).
• Experience optimizing cost and performance of data-heavy workloads.
• Additional languages such as Scala or C++.

Benefits

• Remote-first culture
• Equity
• Unlimited vacation policy
• Universal paid parental leave
• Monthly Hungryroot credit
• Comprehensive health, vision, dental, and life insurance
• 401k with Company Match
• Work from home stipend