Senior Product Manager, AI

ABOUT BUILDOUT Buildout is the AI deal engine for CRE brokerages, automating every step from first contact to commission. While brokers focus on relationships and winning listings, Buildout handles the workflows behind the scenes, turning manual processes into intelligent, scalable systems. Trusted by over 50,000 brokers, Buildout powers more profitable deals from lead to close. Learn more at www.buildout.com http://www.buildout.com. THE OPPORTUNITY Buildout is building the Broker Operating System for commercial real estate. You will own the strategy and roadmap for AI, the AI assistant embedded across the suite: the skills it performs, the judgment of when it should use AI reasoning versus the platform's own exact calculations, and the quality bar it ships against. You will also bring AI into the marketing and document creation workflows that made Buildout the leader in commercial real estate, helping brokers build the materials that win and sell listings, so they get to market and pitch faster. You will work day to day with engineering and design. HOW YOU'LL CONTRIBUTE AT BUILDOUT - Set the vision, strategy, and roadmap for AI: decide which skills we build, in what order, and why. - Be customer facing: spend real time with customers, watch how they actually work, test AI skills against their live workflows, and carry what you learn directly into the roadmap and eval sets. - Prioritize the backlog against broker value, technical feasibility, and quality risk, and run it from concept through launch and iteration. - Define what "good enough" means: build and maintain the eval sets and quality bar each AI skill ships against, and review regressions before every release. - Decide deterministic vs LLM per skill. Know when a rule-based engine beats a model (our next-step recommender is a deterministic engine, not the LLM guessing). - Own prompt, context, and retrieval design and improve it against measured evaluations rather than gut feel. - Set the guardrails: grounding in real records, permission-scoping, confirm-before-action, hallucination controls, and the rollback plan for non-deterministic failures. - Turn broker workflows into skill specs an engineer can build from: trigger, what it reads, what it writes, guardrail, surface. - Ask sharp questions and unblock engineering. - Measure dual metrics: product outcomes (adoption, task success) and model quality (accuracy, groundedness, regression rate). Watch latency and cost against quality. WHAT MAKES A GREAT CANDIDATE - You get outside the building, because the best product decisions come from customer conversations. You regularly interview users, observe workflows, and validate assumptions before committing engineering time. - You're an AI pragmatist, excited by AI but disciplined enough to know when deterministic logic is the better answer. - You're a builder, not just a planner, using tools like Claude, Cursor, or Replit to prototype ideas and learn quickly. - You want evidence, not opinions, defining success upfront and using customer feedback and data to guide product decisions. - You're comfortable in the gray, making thoughtful decisions despite ambiguity, tradeoffs, and rapidly evolving technology. - You're equally strategic and hands-on, connecting long-term product vision with the tactical work that gets products shipped. - You also have: - 5+ years in product management, including 1 to 2 AI/ML or LLM-powered features shipped to real users. - Hands-on experience with rapid prototyping tools (Claude, Cursor, Replit). - Comfort with prompt and context engineering, retrieval-augmented generation RAG, plus a practical grasp of what LLMs can and cannot do. - Strong product discovery skills, including customer interviews, problem validation, and translating insights into roadmap priorities. - Experience partnering closely with Engineering and Design to ship high-quality software in a fast-paced environment. - Enough technical depth to trade off model, data, and latency with…