AI-personalized grocery, recipe and supplement delivery for healthy living
Hungryroot is a New York-based grocery and wellness delivery company that personalizes food and supplement recommendations with machine learning. Its stack pairs Databricks, Spark, MLflow and PyTorch with optimization solvers like Gurobi, CPLEX and PuLP, a sign that pricing and supply decisions run on models. Senior-heavy, accelerating hiring tilts toward operations and data, with produce quality, supplier accountability and pricing experimentation as recurring themes.
Hungryroot delivers healthy groceries, simple recipes and supplements to households, tailoring recommendations to each customer's goals, lifestyle and budget. Founded in 2015 and headquartered in New York, the privately held company employs 201–500 people. Its product runs on Python services built with Django, Flask and FastAPI alongside Ruby on Rails, deployed on AWS with Docker, ECS, EKS and Terraform. Analytics runs through Looker, Amplitude, Heap and Google Analytics, and finance runs on NetSuite. The company hires in the United States and Canada.
Hungryroot uses Python, Django, FastAPI and Ruby on Rails on AWS (ECS, EKS), with Databricks, Spark, MLflow, PyTorch and scikit-learn for ML, Looker for BI and NetSuite for finance.
Yes. Hiring is accelerating and skews senior, led by operations and data roles with some engineering and finance, across the United States and Canada.
Current initiatives include pricing experiments and reporting, a real-time decisioning engine, ML model versioning, automated KPI datasets, and produce quality and supplier programs.
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Hungryroot's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.