Senior Machine Learning Operations Engineer

About the Role

We’re hiring a Senior Machine Learning Operations Engineer to join Hungryroot’s Data Science team. Our team owns the production systems that power grocery recommendations and box personalization for Hungryroot customers.

Our platform combines Python services, FastAPI APIs running on AWS, Spark pipelines on Databricks, and machine learning models that feed a real-time decisioning engine. The system is actively evolving, and we’re investing in the engineering foundations that will let it scale and adapt with the business.

You’ll partner closely with data scientists, operations researchers, and product engineers to build reliable, extensible systems for model-driven personalization. This is an opportunity to shape the architecture behind a core part of Hungryroot’s customer experience.

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.

Qualifications

• 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++.