Senior Backend Engineer, Shopping Agents

sagelabs.ai; San Francisco (CA); On-site; USD 170,000 - 260,000; Full time

Benefits offered by this job

Meaningful equity

Medical, dental, and vision coverage

San Francisco based

Job summary

Sage AI Labs in San Francisco is hiring a Senior Backend Engineer to own the checkout agents and the scale-out infrastructure. You will design, deploy, and operate systems that process live transactions with real carts and payments.

You will tackle ingestion at scale, embedding-based search, and ranking across a constantly changing catalog, collaborating with the CTO and cross-functional teams. This is an onsite San Francisco role with meaningful equity and strong growth potential.

Qualifications

• 5+ years building backend and infrastructure systems.
• Strong Python proficiency.
• Experience owning production systems end to end.

Responsibilities

• Own checkout agents infrastructure and deployments.
• Handle ingestion at scale across multiple sources.
• Implement search, retrieval, and ranking components.
• Ensure reliability, observability, and on-call readiness.

Skills

Python

Backend

Distributed systems

Job description

Senior Backend Engineer, Shopping Agents

Sage AI Labs · San Francisco · Full-time · [Onsite]

About us

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.

About the role

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.

What you'll own

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.

What we're looking for

• 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

Nice to have

• 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

Why this role

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.

Compensation and benefits

• Meaningful early-stage equity

• Medical, dental, and vision coverage; other benefits

• San Francisco based