Technical Product Manager, Product.ai
AI Agents
$300-425k
OTE
$200-260k
BASE
More information about location
5 days a week in office (Santa Monica, CA)
Verification layer for AI commerce
Be an early applicant
Verification layer for AI commerce
21-100 employees
Be an early applicant
$300-425k
OTE
$200-260k
BASE
More information about location
5 days a week in office (Santa Monica, CA)
21-100 employees
Company mission
To separate genuine product knowledge from the flood of AI-generated marketing noise now dominating the internet.
Role
Who you are
- The craft you must already own: writing a spec precise enough that an agent or an engineer could build the right thing from it, and reasoning about an API as a product. Comparable experience we accept: platform PM, developer-tools PM, or infrastructure PM work, even without "AI" in the title
- How you think. You form a working model of a system by reading its interfaces, not just its screens. You notice where your model is wrong and update fast. You can take a rough signal and reason your way to a scoped problem, without waiting for someone to hand you a fully written ticket. You write clearly, because a spec that isn't clear can't be graded
- How you work. You move between an architecture conversation with an engineer and a spec an agent can build from, same day, without needing a translator in the room. You treat agents as leverage, and you verify what they hand back yourself; you direct coding agents inside Cortex most days. Compute is cheap here. A redo cycle from a vague spec costs real time
- What you've probably built. You've shipped an API or a developer-facing surface with real external callers, or you've owned an eval or QA rubric a team actually shipped against
- Adjacent roads count: backend engineering with product ownership, dev-tools PM, or infrastructure PM work where the customer never saw a screen. We care about the artifact and the reasoning more than where you did it
- If the work above reads like yours but your resume is unconventional, apply anyway. We hire on the work and the reasoning, not the pedigree
What the job involves
- We have one product-leader seat open, and we're running it as several doors so the level of the hire follows the person, not the job title. This door is for the technical-side path: an operator who came up through engineering, or who is technical enough to write the spec an AI agent loop is graded against
- AI shopping agents now call outside tools directly, through a protocol called MCP. Increasingly, the customer for a piece of our product is another AI deciding whether to call us
- Someone has to own that surface: the API and MCP layer agents use, the definition of done that decides whether an agent's output ships, and the cost of every feature as a hard constraint on the spec. You work directly with the founder
- Agent-callable product surfaces. An API and MCP layer isn't a backend detail here. It's a product with its own users, except the user is an AI agent instead of a person, and the interface contract is the whole product
- The spec as the evaluation. In a system where agents write the code, the spec has to double as a rubric something can be graded against. "Good" stops being a design opinion and becomes a testable definition you author
- Cost as a spec-level constraint. Every feature carries a cost per query, and you size that cost into the spec itself, the way an earlier generation of PM sized headcount into a roadmap
- Knowledge-graph answers, encoded for a machine reader. A person reading a verdict page absorbs nuance from layout and tone. An agent calling our API can't; it needs cost, provenance, and confidence expressed as fields it can parse and act on without a human in the loop. You decide what that schema carries and what it leaves out
- What makes an agent choose to call you. An agent picks an endpoint the way a person never had to: on latency, schema stability, a trust score it can verify, and whether a version change will break its caller silently. You design for that decision
- Cortex, the shared AI brain the company runs on. You'll work inside it daily, directing coding agents the way our founder does, and since August any operator here can change the rules the company runs on, live, without waiting on him
- A fast-moving spec surface. The rules an agent is graded against today will not be the rules in a quarter. You rebuild the rubric as the system learns, instead of treating a shipped spec as finished
- The agent-facing product. The API and MCP surface AI shopping agents call: what it returns, how it versions, and what breaks a caller's trust. You write the interface contract the way another PM writes a screen
- The eval-gated definition of done. For every feature you spec, you also write the rubric an agent loop is graded against before its output ships. The eval lives inside the spec from the first draft
- The cost line, per feature. You model cost per query into every spec you write, and you can defend the number, not just the feature
- The developer and agent relationship, on the product side. The commercial relationship with the platforms and developers who call this API belongs to our commercial seat. The shape of what they're calling, its reliability, and its roadmap belong to you
- Your seat charter. Within your first quarter you co-sign a charter for this seat. It names one machine-checkable number that proves the seat is working, and a written split of what you decide freely versus what you bring to the founder
- What you'll grow into here: eval design as a first-class product skill, and directing coding agents as your default production system
Application process
- We don't run traditional product-manager interviews
- Async video screen. About 15 minutes, on your own time. It replaces the recruiter screen
- Calls with company stakeholders. Short conversations with the team
- Conversation with the founder. How you model the system above, and where you'd start
- Paid work trial. Real work in our real environment, at your stated rate, taking a live spec problem from intent to something an agent could be graded against
- Complete a brief video questionnaire to apply for this role. It takes 12 minutes to calibrate initial compatibility. Beyond that, just bring clarity of mind and a readiness to share who you are
Our take
Artificial intelligence has come on strides in recent years, but many assistants still struggle with relatively simple tasks. For example, AI-powered shopping assistants have frequently been found to supply conflicting information regarding prices, product specifications and more.
Product.ai is building what it calls the verification layer for AI commerce, to combat inaccurate and misleading product overviews. The platform can do things like check whether coupons actually work, verify the latest prices, and decipher whether claims about products are true.
Formerly known as Demand.io, the company's earlier focus was on creating innovative eCommerce experiences, but its rebrand to Product.ai in 2026 cemented its pivot to creating the truth layer for eCommerce. This mission answers a growing problem for consumers, as increasing AI marketing noise drowns out true reviews - meaning there should be ample opportunity for Product.ai to make an impact.
Steph
Company Specialist at Welcome to the Jungle
Company
Company benefits
- Full health coverage: All premiums covered for you and your family. Medical, dental, vision.
- Daily catered lunch: For the LA team. One less thing to think about during the workday.
- Unlimited PTO: That we actually expect you to use. Recharge is not optional, it is structural.
- Remote flexibility: An LA headquarters with full remote support. Work where you do your best thinking.
- Free garage parking and commuter assistance
Company HQ
Sawtelle, Los Angeles, CA
Leadership
Michael Quoc
(Founder & CEO)Experience as Senior Product Manager for Yahoo! Inc. from 2002 to 2006 and as Director of New Products from 2006 to 2008.
Brian Stanback
(Chief Architect)A software Engineer with a passion for building highly interactive web applications. Brian has over 15 years of experience as a Web Developer, Software Architect, and Database Engineer.
Jimmy Doheny
(Director of Operations)Jimmy joined Product.ai in 2017 as a Partner Marketing Manager. They then moved into the role of Senior Manager of Revenue Operations in 2019 and Director of Operations in 2021.
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