Senior Fullstack Engineer (Product)
Posted Updated
- This is a hands-on IC role
- Everyone builds
- Everyone ships
- The problems here don’t stay in their lane
- One fix might take you from a rendering bug to a prompt regression to a broken retrieval plan to a backend endpoint
- You follow it wherever it goes
- Our team is not looking for the conventional AI resume or an exact stack match
- We’re looking for product judgment and first-principles thinking
- Someone who cares about their craft
- Build the chat experience diners talk to every day
- Own the agent runtime end to end
- Ship new tools the model can call, and the tests that pin their behavior
- Make answer quality measurable: evals, tracing, and real user feedback
- Improve retrieval so the agent recommends the right place
- Take the product from answering questions to completing tasks
- Keep turns fast, cheap, and reliable on mobile networks
- Get work in front of real diners fast, with product and design
- Taste in evaluation: you know the difference between fixing a failing eval and gaming one
- Real depth with LLM application development: tool calling, context management, retrieval, streaming
- Product judgment about what makes an agent feel trustworthy, fast, and worth coming back to
- You use current models seriously and have your own ideas about how agent systems should work
- 5+ years shipping production software, owning features end to end
- Able to debug across the stack: a streaming client, application code, SQL, and the boundaries between services
- Comfort with ambiguity and high ownership on a small team
- Strong React and TypeScript, fluency in Python, and willingness to work in Kotlin/Spring when the answer lives on the platform side
- Solid Postgres and data modeling instincts, including a feel for what makes retrieval good or bad
- Agent evals and LLM observability tooling (Langfuse, Braintrust, LangSmith)
- Vector and geospatial search in Postgres (pgvector, PostGIS)
- Shipping AI into a native mobile surface: streaming, cold starts, bad networks
- Frontier model APIs: tool use, prompt caching, extended thinking
- Restaurants, hospitality, travel, or another domain where recommendations carry real-world consequences
- Datadog, Amplitude, LaunchDarkly, AWS, or Terraform