Senior Fullstack Engineer, Product

About the Role

Think about how people dine: where they go, how often they come back, whose taste they trust, which rooms they have a relationship with. We're building Blackbird to answer those questions the way a well-connected friend would. We need a product-minded fullstack engineer to own it.

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.

Responsibilities

• 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.

Requirements

• 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.
• Real depth with LLM application development: tool calling, context management, retrieval, streaming.
• 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.
• Taste in evaluation: you know the difference between fixing a failing eval and gaming one.
• Product judgment about what makes an agent feel trustworthy, fast, and worth coming back to.
• Comfort with ambiguity and high ownership on a small team.
• You use current models seriously and have your own ideas about how agent systems should work.

Nice to Haves

• 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.

Pay Transparency Notice & Benefits

• Depending on your work location and experience the target annual salary for this position can range from: $180,000-$220,000 USD.
• This range does not include the equity component of the overall compensation package.
• Disclosure in accordance with New York City's Pay Transparency Law.