Senior Machine Learning Operations Engineer

Role Overview

Hungryroot is hiring a Senior Machine Learning Operations (MLOps) Engineer on the Data Science team. You’ll build and operate the production systems that power grocery recommendations and box personalization, partnering with data scientists, operations researchers, and product engineers.

Responsibilities

• Design, build, and operate scalable backend services, APIs, and data pipelines.
• Improve reliability, performance, and observability of production ML and optimization systems.
• Own the end-to-end path from trained model to production:
  • Model versioning/registry (e.g., MLflow)
  • Safe rollout and rollback
  • Monitoring for data quality and model drift
• Build clean interfaces to integrate new ML models and decisioning capabilities safely, including experimentation and feature-flag tooling.
• Strengthen engineering foundations across a growing codebase:
  • automated testing, type checking, CI/CD, infrastructure as code, documentation, and system design
• Profile data-heavy services and pipelines and reduce execution time and memory footprint.
• Collaborate to translate business needs into robust technical solutions.

Requirements

• 5+ years in MLOps, ML engineering, or DevOps focused on production ML infrastructure.
• Strong Python and SQL; Bash for automation/tooling.
• Experience designing and operating backend services/APIs (e.g., FastAPI) with focus on reliability, latency, and scalability.
• Hands-on experience with Databricks and Spark (jobs/workflows; Unity Catalog a plus).
• Experience with MLflow or comparable model lifecycle tooling (registry, versioning, experiment tracking).
• CI/CD for ML/data systems: Git, GitHub Actions/Jenkins, Databricks Asset Bundles.
• Infrastructure as code (e.g., Terraform).
• Solid AWS fundamentals: IAM, networking, compute/cluster management, containerized workloads (Docker; ECS/EKS).
• Production observability: metrics/logging/alerting plus ML-specific monitoring (data quality, model drift).

Nice to Haves

• Familiarity with recommendation/personalization and/or operations research systems, especially productionizing them.
• Experience with optimization solvers/OR tooling (e.g., Gurobi, OR-Tools).
• Experience integrating experimentation and feature-flag platforms (e.g., Statsig) into production ML services and data pipelines (preferably warehouse-native).