About Us

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.

It’s the easiest way to eat healthy, achieve your goals, save time, and discover new foods. We believe food is the foundation of health, convenience should not mean compromise, and that everyone is unique in how they eat and live. That’s why we’re building a future in which healthy living is both easy and enjoyable.

Hungryroot is a distributed team of top talent across 28+ U.S. states. While we have a headquarters in New York City, our remote-first culture emphasizes collaboration, team-building, and flexibility. Expect regular virtual team events, strong ownership and accountability, and an annual company retreat.

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++.

About Us

About the Role

Responsibilities

Qualifications

Nice to Haves

<

Senior Machine Learning Operations Engineer at Hungryroot