AI Chief of Staff

The operational right hand to the founder. You own the company's operational outcomes end to end — and run AI agents against them. Product.ai is the verified truth layer for shopping — the intelligence that tells you what's actually true about a product, including when not to buy. Profitable. Bootstrapped. No outside investors. No board. 20 people outbuilding companies 10× our size. Strong people find us and keep finding us — they apply over months and years, because the field moves fast and the exact profile we need moves with it. Why This Role Exists The CEO runs a dozen concurrent AI agents while running the company: own a falsifiable outcome, set an independent check the agent can't fake, point the agents at it, verify what comes back, ship. The operational layer of the company deserves the same operating model — people operations, the ownership-program mechanics, recruiting throughput, vendor and office operations, company cadence — but today it runs on the CEO's margins. This role gives that layer an owner. You consolidate the company's operational outcomes under one person who owns them end to end and runs agents against them. You are measured on outcome movement and the decision capacity you return to the CEO — not on hours logged or meetings attended. The System You'll Need to Model A recruiting funnel processing ~1,400 candidates, with AI evaluation at the top and human throughput as the binding constraint. Candidate-to-decision latency is the number that matters, and the mid‑funnel — screen to decision — is where people stall. That mid‑funnel is your system to fix, and it is the first thing you own. CEO attention as the scarcest resource in the company. Time architecture, decision queues, delegation physics: which decisions route to him, which route to you, which route to an agent with a check on the end. Nobody owns this system today. An ownership program with real equity mechanics — Profits Interest Units, annual tender offers, profit sharing tied to free cash flow. Real ownership needs operational rails: grant cycles, clean records, tender logistics, communication people can trust. A company that runs on AI agents at scale, where the return on a dollar of compute is a tracked metric. Every agent run is instrumented for what it consumed and what outcome it moved; the discipline that follows is steering large compute budgets toward business outcomes in real time. A company operating system where the work is legible. Outcomes are falsifiable, work is captured in consent‑based records, and architectural decisions are registered as law — a three‑tier system of constitutional rules, specifications, and code, enforced by automated gates rather than memos. Your job runs through this system, not alongside it, and it all sits on a shared knowledge base your agents query to answer their own questions. A company that revises its own operating model on a regular cadence. You won't get briefs. You'll model where the company is going and build ahead of it. What You Will Own Operational outcome loops. You own falsifiable operational outcomes — each with a test a stranger could run — and move them by running long‑lived AI agents (our practice is 1‑4 hour unattended runs) against them. The agents do the research, drafting, and reconciliation; you set the outcome, design the check that stays independent of the work, and judge what ships. Recruiting pipeline throughput. Candidate‑to‑decision latency is your number. AI handles top‑of‑funnel evaluation; humans are the constraint in the middle. Instrument the funnel, find the queue, clear it — with agents where they work and your own judgment where they don't. Ownership‑program operations. Our equity is real — PIUs at $0 strike, annual tender, cash profit sharing — and real ownership needs rails people can trust: grant cycles, vesting records, tender logistics, clear communication. You build and run them. Company cadence, office, and vendors. The rhythms that keep 20 people coordinated without bureaucracy, plus contracts, vendors, and the physical environment — the layer every company needs, run here like a product. As this scope scales, a Workplace Operations Lead can report into it. Who You Are You independently form working models of complex systems, notice where the model is wrong, and update fast. You turn ambiguity into instrumented systems: when a process is fuzzy, you make it measurable before you make it better. You treat CEO leverage as the product — every system you ship is judged by the decision capacity it returns — and you make good calls in the gray area without a defined path. You move between company strategy and operational implementation without getting stuck at either altitude: a comp‑policy question in the morning becomes a working tracking system by evening. You write clearly, because clear writing is evidence of clear thought. And you build with AI yourself — you can do this job by hand and prove it, and you direct agents the way the CEO does: you set the outcome, design the check, and own the verdict on what they produce. You think in tests and guardrails — you trust a result once you've designed the check that proves it. The expensive thing is a redo cycle, never compute — spending it well, toward outcomes, is the job now. Who This Isn't For Who this isn't for. This role fits someone with high agency for whom ambiguity reads as raw material and shipping the CEO a week of reclaimed attention feels like shipping product. It's the wrong role if you need the job handed to you as a task list — that list doesn't exist; you write it. It's wrong if you want to instrument the funnel, clear the queue, and own outcomes only after someone else defines the process and the timeline. It's wrong if your core skill is managing a calendar, or if you optimize for proximity to power — being near decisions instead of owning outcomes. And it's wrong if you'd rather route the building to someone else than direct agents yourself and stand behind what they produce. How We Evaluate Written artifact. A system you built — an automation, a runbook, a pipeline you instrumented — with the before and after. Writing quality is the first filter. Video screen. Brief and async: 5‑6 questions, about 15 minutes, whenever works for you. Calls with company stakeholders. Short conversations with key members of the team. Conversation with the founder. How you model a company's operational needs — and where you'd put the first agent to work. Paid work trial. Two to four weeks of real work in our real environment. We watch four things: how you ground yourself in our systems, whether you write the spec before the build, how you verify what your agents produce, and whether your self‑assessment is honest. Compensation & Ownership Total first‑year comp: $300,000 – $400,000 (base + equity + profit sharing). Base: $200,000 – $250,000 — top of market for operations leadership. The rest is real ownership: Profits Interest Units (PIUs) — Class B Membership Interests at $0 strike, real ownership day one, capital‑gains treatment; annual pro‑rata profit sharing from free cash flow; annual tender liquidity; 100% family premium coverage; effectively unlimited token budget, steered by ROI, never capped. This is a partnership structure. Based in Los Angeles, California — hybrid, with flexibility. For the right builder, we're open to remote. Complete a brief video questionnaire to apply for this role. It takes 12 minutes to calibrate initial compatibility. Your only required preparation is reading the guide linked above. Beyond that, just bring clarity of mind and a readiness to share who you are. Apply at product.ai/join/ai-chief-of-staff — include your strongest written artifact: something you built that made an organization run better, and how you knew it worked. #J-18808-Ljbffr Product.ai