## What are you working on?
BetterBasket is building the “Cursor for food & beverage” — AI systems that understand, explain, and run merchandising decisions for grocers, starting with pricing.
Pricing in grocery is still driven by spreadsheets, gut feel, and fragmented data. We’re replacing that with systems that ingest competitive data, map products across retailers (including private label and fresh), model demand and substitution, and generate exact revenue and margin impacts for every decision — then execute.
We’re already responsible for pricing billions of dollars of grocery items annually, working with leading retailers across North America and LatAm. From day one, you’ll work directly on production systems used by real operators making high-stakes decisions every week.
BetterBasket is backed by Y Combinator, Aito Capital, and founders and executives from companies like YouTube, Uber, Amazon, Tesco, and Stubhub.
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## Who should NOT apply
* You want to be fetching coffee or shadowing meetings all summer — you’ll ship real systems used in production by grocers making pricing decisions
* You want a scoped “intern project” with a final presentation — you’ll be working with real customers who have daily, urgent needs
* You want to spend your time reading and discussing research papers — you’ll be expected to turn the latest model releases into real-world value, quickly
* You want a structured intern program with events, career exploration, happy hours — we believe the best way to learn is to build and ship (but we do have Friday happy hours at the beer garden)
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## What can I work on?
You’ll work on core systems that power automated decision-making in grocery:
* How do we extract and normalize messy, unstructured data from hundreds of retailers into a unified product knowledge graph?
* How do we match products across retailers — including private label, fresh, and imperfect data — with high precision?
* How do we model demand, substitution, and elasticity to predict the impact of price and promotion changes?
* How do we turn recommendations into actions — automatically triggering and executing price changes?
* How do we build systems that explain _why_ something happened in a store — and what to do next?
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## What technologies do you use?
### Core Competencies
* Python (data systems, ML, concurrency)
* HTTP / REST APIs
* Javascript (Typescript, React, Node)
* SQL (query optimization, large-scale data handling)
* Cloud / DevOps (Azure, Kubernetes, Docker, Redis)
### Nice to Have
* Scraping (Scrapy, Selenium, Playwright)
* LLMs / agentic systems
* Data modeling / entity resolution
* Product design (Figma)
* Distributed systems / inference at scale
Visa sponsorship is not available for this role.
