AI Tooling Engineer

Whatnot Whatnot · Consumer · San Francisco, CA · Engineering

Senior AI Engineer to build internal tools, prototypes, and business workflows that put AI into the hands of every team at Whatnot. This role involves owning ambiguous, cross-org bets end-to-end, shipping working software fast, hardening what works, and scaling how Whatnot gets value out of AI. The engineer will define reusable patterns and shared infrastructure, integrate AI tools with internal systems, and stay ahead of the AI landscape. This is a builder role focused on applying off-the-shelf AI, not model training or research.

What you'd actually do

  1. Own ambiguous, cross-org AI bets end-to-end—identify the highest-leverage problems across the company, decide what to build, and drive it from prototype to durable production tool
  2. Build and ship a high volume of internal apps, prototypes, and automations—going from a vague problem to a working tool in days, then iterating with users toward production quality
  3. Define the reusable patterns and shared infrastructure the org builds on—reference architectures, internal libraries, MCP servers, eval harnesses, and templates that let others move faster and safer
  4. Embed directly with teams across CX, Trust & Safety, ops, GTM, and EPD to find high-leverage problems, then build the solution alongside them
  5. Wire AI into real business context—building RAG and retrieval pipelines, MCP servers, and agentic workflows grounded in Whatnot's data, with appropriate PII and access controls

Skills

Required

  • 4 + years of industry experience at scale
  • 1+ years of industry experience at scale
  • extensive hands-on experience building with the current generation of LLM products (Anthropic, OpenAI, Google)
  • prompt engineering
  • RAG
  • MCP
  • agents
  • evals
  • Systems-integration chops
  • connecting AI to real data and tools through APIs, webhooks, and connectors
  • reliability, latency, cost, and access controls
  • pattern-setter and force multiplier
  • mentored other engineers
  • raised the technical bar for an org
  • strong communicator
  • translate their problems into software
  • teach them to build for themselves
  • Low ego, high agency

Nice to have

  • frontend
  • backend
  • data

What the JD emphasized

  • 4 + years of industry experience at scale
  • 1+ years of industry experience at scale
  • extensive hands-on experience building with the current generation of LLM products
  • production patterns around them: prompt engineering, RAG, MCP, agents, and evals

Other signals

  • building internal tools
  • shipping working software fast
  • defining reusable patterns and infrastructure
  • integrating AI into business context
  • staying ahead of the AI landscape