AI Tooling Engineer
Job in
San Francisco, San Francisco County, California, 94199, USA
Listed on 2026-06-12
Listing for:
Whatnot
Full Time
position Listed on 2026-06-12
Job specializations:
-
IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Job Description & How to Apply Below
Whatnot is the largest livestream shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.
As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.
We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.
The Role
We're looking for a Senior AI Engineer to build the internal tools, prototypes, and business workflows that put AI into the hands of every team at Whatnot-and to set the patterns the rest of the company builds on. You'll own ambiguous, cross-org bets end-to-end: finding real problems, shipping working software fast, hardening what works, and scaling how Whatnot gets value out of AI.
This is a high-velocity, high-judgment builder role. You've shipped a lot of apps and prototypes-you're someone who can sit with a CX lead or an ops manager, understand their workflow in an afternoon, and have a working prototype in their hands by the end of the week. But you also operate above any single project: you decide where the leverage is, define the reusable patterns and infrastructure others adopt, and raise the bar for how the whole org builds with AI.
You move fluidly across the stack (frontend, backend, data, and the AI layer) and are deeply fluent in the current generation of AI tools and the production patterns around them-prompt engineering, RAG, MCP, agents, and evals.
This is not a model-training or research role. You won't be training models-you'll be building the tools, workflows, and integrations that make off-the-shelf AI dependable and useful inside Whatnot's systems and processes.
• What You'll Do
- 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
- 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
- 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
- Embed directly with teams across CX, Trust & Safety, ops, GTM, and EPD to find high-leverage problems, then build the solution alongside them
- 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
- Integrate AI tools with internal systems and data sources via APIs, connectors, and event-driven workflows so automations act on real state, not toy inputs
- Scale the leverage-package successful builds into reusable skills and playbooks, and level up the whole company through enablement sessions, boot camps, and mentorship of other builders
- Stay ahead of the AI landscape, evaluating and bringing in new models, tools, and patterns as the ecosystem evolves, and making the build-vs-buy calls
- A prolific builder with senior judgment-you ship fast and iterate with real users, and you know where not to invest; you measure yourself by problems solved, tools adopted, and leverage created
- Deep applied-AI fluency-extensive hands-on experience building with the current generation of LLM products (Anthropic, OpenAI, Google) and the production patterns around them: prompt engineering, RAG, MCP, agents, and evals
- Systems-integration chops-you've…
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