Senior AI Product Engineer

Job description

About the company Turning marketing into a market-domination weapon, we created a new category — the marketplace for future customers. Businesses shop for leads as they shop for products in e-commerce: you see everything about a lead before you buy — full transparency, control, and predictable outcomes. We're the #1 lead marketplace in the USA, and we're accelerating. Role overview We're looking for a Senior AI Product Engineer to build the AI operating layer behind our marketplace. You take a valuable but unclear business problem — improve lead quality, cut refunds, automate a manual workflow, extract intelligence from calls — and turn it into a dependable production system end-to-end: investigate, define success, design, build, deploy, and improve on real usage data. You are a one-person AI product team for high-leverage problems, not a spec-taker. We are expanding beyond real estate into new service-business niches, and growth is powered by reusable software and automation — not proportional growth in manual work. Every system you ship either compounds into that leverage or exposes where it leaks — no in-between. Why this role exists Our growth is not limited by ideas or data — we already run 40K+ members, 750+ verified daily leads, and 50B data points powering AI. It's limited by how fast we can turn ambiguous, high-value problems into shipped AI systems that actually move business metrics. We need a builder who owns the complete production outcome — not a prompt tinkerer, not a researcher, not a PM who delegates the build. Requirements: Strong record of building and shipping production software end-to-end, ideally in a SaaS, marketplace, or workflow product — with real users and real revenue on the line. Strong Python and/or TypeScript, plus solid backend fundamentals: APIs, databases, auth, data models, testing, and deployment. Hands-on experience shipping LLM-powered functionality using model APIs, structured outputs, tool/function calling, retrieval, context engineering, and agentic workflows. Production automation experience: webhooks, events, queues, schedules, retries, idempotency, monitoring, and failure recovery — not just happy-path demos. Practical evaluation skills: test sets, traces, quality metrics, regression checks, feedback loops, and root-causing real production failures. Daily fluency with AI coding agents (Claude Code, Codex, Cursor,…