What You'll Work On
- AI-native product surfaces. Chat and search experiences over property records - streaming responses, citations and provenance display, multilingual UX, and graceful handling of model uncertainty. The hard part isn't calling an API; it's designing how AI output earns user trust.
- Intelligence products people pay for. Verification packs, diligence reports, and the purchase flows behind them — packaging AI-derived judgment into outcomes users trust enough to buy, where clarity and reliability directly drive revenue.
- Full-stack feature ownership. Frontend (React/React Native), backend services (Python/Node), and the APIs that connect product to our ML and data pipelines.
- Evals as product infrastructure. Building evaluation suites that measure whether Terra's agents actually help users — reading transcripts to find capability gaps, defining target behaviors, turning production failures into test cases, and giving the ML team evidence-backed priorities instead of vibes.
- Speed with judgment. Prototyping with AI coding tools, instrumenting what you ship, and killing or doubling down based on usage data.
Your First 90 Days
Days 1–30: Ship and absorb. Land your first production change in week one. Talk to users — buyers, brokers, lenders — and read agent transcripts until you can name the top three places Terra loses user trust. Own one product surface end to end.
Days 31–60: Own an outcome. Take a revenue- or trust-critical flow — a verification product, the conversational experience, a diligence report — and move its metric: conversion, completion, satisfaction, or eval pass rate. Stand up or extend the eval/instrumentation that proves it.
Days 61–90: Set direction. Propose the next product bet backed by what you've seen in usage data and transcripts, and start building it. By now you should be the person the team asks "what should we do here?" for your surface — with evidence, not opinions.