AI Chief of Staff

Posted 4 Hours Ago
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Metropolitan, CA, USA
In-Office
200K-400K Annually
Senior level
Artificial Intelligence • Big Data • Consumer Web • eCommerce
Product.ai is the truth layer for commerce.
The Role
Owner of company-wide operational outcomes: run and verify long-lived AI agents to move measurable metrics (first: hiring candidate-to-decision latency), build operating cadence, run back-office/finance/vendor operations, design an ownership program, and grow a human-workforce platform. You model complex systems, instrument processes, write clear specs, and ship operational products inside the company AI brain (Cortex).
Summary Generated by Built In
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. SimplyCodes is that truth layer's first proof at scale: the code verification service that shows shoppers the codes that actually work, running at roughly $22 million in revenue and roughly 60% margins. We are 100% founder-owned and profitable, bootstrapped since 2009 - no outside investors, no board. A small team - fewer than twenty operators - outbuilding companies 10× its 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 - the operating cadence, back-office and vendor operations, recruiting throughput, the ownership-program rails, and a growing platform of paid human effort routed to what machines can't do yet - 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.
  • Cortex, the shared AI brain that runs the company. Every operator works through governed AI sessions inside it - every outcome, spec, and record moves through Cortex - and it is the same product family we sell to the outside world. The work is legible by design: outcomes are falsifiable, records are consent-based, and architectural decisions are registered as law, in a three-tier system of constitutional rules, specifications, and code, enforced by automated gates rather than memos. Your job runs inside Cortex, not alongside it, and it answers its own questions from more than 8,600 documents.
  • 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.


If reading that energizes you, keep going. If it feels overwhelming or underspecified, this isn't the right fit.

What You Will Own

  • Operational outcomes, with one co-signed number. 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. This is the model we run here: within your first quarter you co-sign a seat charter with one machine-checkable number that proves the seat works. Candidate-to-decision latency in the hiring funnel is one such number, and it is the first you'll own.
  • The operating cadence. The rhythms that keep the team coordinated without bureaucracy - the weekly and monthly beats, the decision queues, and the delegation lanes that route each call to the CEO, to you, or to an agent with a check on the end.
  • Back-office, finance, and vendor operations. Contracts, vendors, spend, and the physical environment, plus the rails a real ownership program needs - grant cycles, vesting records, tender logistics. You run the whole layer here like a product, not a chore.
  • The human-workforce platform. A growing system that routes paid human effort to the work machines can't do yet. You design where the line sits between an agent and a person, and you move that line as the machines get better.


Decision authority is explicit, not implied. The seat charter carries a written authority split - what you decide freely, and what you propose for the CEO to sign - alongside vendor-spend thresholds and sign-off lanes. We put it in writing in your first 30 days.

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.

What you've probably built: operational systems at a company moving from scrappy to structured - an automation that retired a manual process, a hiring pipeline you instrumented end to end, a vendor or finance workflow with real money moving through it, agents you designed and verified yourself. We care about the artifact and the reasoning more than where you did it.

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

We don't run traditional chief-of-staff interviews.

  • Async video screen. Brief and self-recorded: 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 paid work in our real environment, on a real problem. We say so up front on purpose: paying well for real work respects your time and draws high-agency people. 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.


  • If the work above reads like yours but your resume is unconventional, apply anyway. We hire on the artifact and the reasoning, not the pedigree.

    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 from day one, capital-gains treatment; annual pro-rata profit sharing from free cash flow; annual tender liquidity; 100% family premium coverage; and an effectively unlimited token budget, steered by ROI, never capped.

    This is a partnership structure, built to mint partners. When the company wins, you win - in real, liquid dollars, every year.

    Based in Santa Monica, Los Angeles - in person, five days a week. The rooms are real rooms.

    Skills Required

    • Proven experience designing and running long-lived AI agents and instrumenting their outputs
    • Track record owning recruiting funnel operations and reducing candidate-to-decision latency at scale
    • Experience building operating cadence, decision queues, and delegation lanes for senior leadership
    • Experience managing back-office, finance, vendor operations, and grant/ownership program rails (PIUs, tender logistics)
    • Ability to design machine-checkable tests and verification checks for outcomes
    • Strong written communication and clear-spec writing skills
    • High agency; comfortable with ambiguity and autonomous decision-making
    • Practical experience directing agents and humans; able to build by hand and automate reliably
    • Willingness to complete a paid 2-4 week work trial in the company environment
    • Ability to work in-person five days a week in Santa Monica, Los Angeles
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    The Company
    HQ: Los Angeles, CA
    25 Employees
    Year Founded: 2009

    What We Do

    Product.ai (formerly Demand.io) is the truth layer for commerce. Built on Axiomatic Intelligence — a proprietary adversarial reasoning methodology that stress-tests product claims against physics, economics, and engineering constraints — Product.ai delivers verified purchase verdicts, not summaries. Product.ai tells consumers when NOT to buy. Product.ai emerges from Demand.io, a profitable, bootstrapped AI commerce company whose SimplyCodes platform processes over $1B in annual transaction value with a team of 20. Founded by Michael Quoc.

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    Product.ai Offices

    Hybrid Workspace

    Employees engage in a combination of remote and on-site work.

    Typical time on-site: Flexible
    HQLos Angeles, CA
    Our office is centrally located at the intersection of Santa Monica and Brentwood on a trendy section of Wilshire. Offering expansive views of the ocean to downtown LA, our high rise building sits right next to some of LA's most popular restaurants, cafes, juice bars and brunch spots.

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