Visiting Engineer: eCommerce Infrastructure

career • Mountain View, CA

44d • Onsite

About The Position

Product data is still built for human browsing, not AI-driven discovery. That creates broken search, manual merchandising work, and poor performance on high-intent queries. This role is to build an agent-ready commerce layer for SMB brands: a Shopify app that turns messy catalogs into machine-readable product data, powers natural-language discovery, and exposes clean feeds for AI shopping agents.

What you'll build:
A Shopify app that ingests raw catalog data and normalizes it into structured product attributes such as fit, fabric, style, occasion, compatibility, and use case.
A natural-language discovery layer that handles ambiguous shopper intent better than keyword search.
An agent feed or API that lets external AI systems reason over product data instead of scraping storefront pages.
Merchant-facing QA and correction workflows so the system improves on real catalog edge cases.

What you'll do:
Own the technical build end to end, from data model and enrichment pipeline to application layer and deployment.
Design catalog enrichment systems that mix deterministic structure with model-based reasoning where it genuinely improves outcomes.
Build search, retrieval, ranking, and feedback loops that work for both shoppers and AI agents.
Get the product in front of merchants quickly, identify where catalog quality breaks, and ship fixes fast.
Work with AI Fund's build team on technical direction, product strategy, and wedge expansion.

Requirements

• Strong full-stack execution, especially Python on the backend and modern web frameworks on the frontend.
• Demonstrated experience building GenAI products where you made real decisions about retrieval, orchestration, evals, or guardrails.
• Experience with ecommerce infrastructure, Shopify apps, product catalog systems, semantic search, knowledge graphs, or attribute extraction.
• Product judgment about where automation helps and where merchant control is required.
• You know how to use AI coding assistants and modern GenAI tooling to move faster without outsourcing engineering judgment.
• US work authorization. We are unable to sponsor visas for this role.

Nice To Haves

• Merchandising or search background.
• Experience with product taxonomy, entity resolution, embeddings, or data quality tooling.
• Founder or founding engineer experience.

Responsibilities

• Own the technical build end to end, from data model and enrichment pipeline to application layer and deployment.
• Design catalog enrichment systems that mix deterministic structure with model-based reasoning where it genuinely improves outcomes.
• Build search, retrieval, ranking, and feedback loops that work for both shoppers and AI agents.
• Get the product in front of merchants quickly, identify where catalog quality breaks, and ship fixes fast.
• Work with AI Fund's build team on technical direction, product strategy, and wedge expansion.

Benefits

• Founder-level role with meaningful equity upside if the build leads to a funded company.
• Compensation is intentionally modest during the residency because the upside, if the idea works, is a founder-level role.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed
• Number of Employees: 101-250 employees