Senior Backend Engineer, Shopping Agents
Sage AI Labs · San Francisco ·
- Work mode
- Onsite
- Seniority
- Senior
- Employment
- Full time
- Category
- Backend
- Experience
- 5+ years
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Owns the backend and infrastructure for AI shopping agents that browse the open web and complete real purchases end to end — building resilient checkout agents, large-scale product ingestion, and embedding-based search and ranking in Python, with full ownership of deploys, monitoring, and on-call.
Sage AI Labs · San Francisco · Full-time · [Onsite]
Sage AI Labs is building the infrastructure that lets AI agents transact on the open web. It is one of the few genuinely unsolved problems left in commerce, and whoever solves it sets the defaults everyone else builds on top of.
The company was founded by Sebastian Thrun (founder of Google X and Waymo, Udacity) and is backed by leading venture and strategic investors.
We build agents that shop for you. They find products across the open web and complete the purchase end to end. Real carts, real payments, and real systems built to stop them.
You would own that. Architecture, code, deploys, monitoring, and on-call for systems that transact around the clock, working directly with our CTO.
Our checkout agents. Frontier-model computer-use agents driving live checkouts at scale, plus the machinery that keeps them standing when the ground moves. Every retailer breaks differently and they change without warning. Your job is to make failure rare and recovery automatic.
Ingestion at scale. Take product ingestion from hundreds of sources to thousands. The answer is not more hand-written code. It is infrastructure that generates, validates, and monitors itself.
Search and ranking. Embedding-based retrieval, LLM query understanding, reranking, and caching, across a catalog that never stops changing.
5+ years building backend and infrastructure systems, with strong Python
You have owned production systems end to end: deploys, databases, observability, and the pager. You have been the person who got paged, and you fixed the class of problem rather than the incident
You like adversarial problems. Systems where something on the other side is actively working against you
You take correctness seriously because the stakes are real. These systems spend customers' money, so a silent failure is a wrong order, not a red build
You can move without a finished spec. We are small, and the roadmap changes when the market does
Built AI-first products or LLM-based agents in production, not just in a prototype
Working knowledge of classic ML and deep learning, enough to reason about retrieval and ranking rather than only calling an API
Background in e-commerce, payments, or fraud and risk infrastructure
Very few engineers get to own a category-defining system while the category is still being defined. This is one of those seats, at the point where it still counts as early. A year from now this problem will have a standard answer and someone will have written it. We intend for that to be us.
Meaningful early-stage equity
Medical, dental, and vision coverage; other benefits
San Francisco based
SEA · Singapore