## The company Ooak Data turns company data into training data for AI agents. Frontier labs can train models to reason. They cannot train them to work: navigating a real company's Slack threads, half-finished Notion docs, contradictory Jira tickets, and permission boundaries. That requires real enterprise data, and you cannot synthesize it.
You have
- to source it.
- We plug into enterprise tools, anonymize everything into a structurally identical digital twin, and generate reinforcement-learning environments with expert-level tasks calibrated against frontier models.
- We are a Y Combinator company with 7 figures signed contracts with three frontier AI labs, and we are scaling delivery aggressively over the next twelve months.
- Three founders, full-time since December 2025: Pierre-Louis (CEO, ex-COO in edtech, sold data to frontier labs), Grégoire (CPO, first PM & head of Ops at Epsor through Series B), Thomas (CTO, ex-Head of Data at PayLead, ex-Samsung AI lab). ## Why this role exists Our engineering team ships faster than founders can specify.
- We have four product surfaces, most of them internal, all of them technical, and every one of them decides whether an environment ships this week or next.
- We are hiring our first Product Manager to own the product: what gets built, in what order, and whether it actually made us faster. ## What you will own The full chain, from the moment a CEO agrees to share their data to the moment a frontier lab receives an environment. **Partner experience** * The Data Hub, onboarding and export tutorials a CEO sees when they hand us their company's history.
- Trust and speed at the top of the funnel. **The data pipeline** * Connectors, ingestion and anonymisation.
- How raw Slack, Drive, mail and CRM exports become a clean digital twin.
- You will spend a lot of time with Thomas (CTO) here. **Pharos, our delivery platform** * The internal tool our Ops team lives in to run QA and delivery.
- Throughput is the metric.
- This is where 3 env a week becomes 10. **RL environments** * The thing the labs buy.
- Task design, calibration, what separates a dataset from a real agent training ground.
- You will run two kinds of work at once: short projects that need tight coordination across Tech, Ops and GTM this week, and long ones where nothing exists yet and you get to decide what should. ## Who you are * **A doer.** You know the product methodologies and you use them when they help, not by the book.
- Your instinct when something is slow is to fix it, not to schedule a workshop about it. * **AI-pilled.** You vibe code.
- You prototype before you write a spec.
- You design your own mockups.
- You are curious about how the models themselves are built and trained. * **UX taste, ROI brain.** Our workflows have to be brutally efficient.
- You judge a piece of software or a workflow by efficient it allows us to be or what value it brings to our customers. * **You keep pace.** Our engineers ship a lot.
- You turn that into something coherent rather than slowing it down. * **7+ years in product**, at least some of it technical: data, infra, ML, developer tools or ops platforms.
- Founders and first PMs who built the function from nothing are very welcome. * **Fluent French and English.** Our customers are American, our data partners spread worldwide, our team speaks both. * **Based in Paris.** **The extra that make the difference** * A research streak: you read papers, you have opinions on RL, you have trained or fine-tuned something. ## Terms * **Compensation:** €70-90k + bonus, and 0.2% to 0.5% BSPCE depending on profile. * **Location:** Paris Morning Laffitte, on-site with 1 to 2 days WFH. * **Start date:** as soon as you can. * **Perks:** Alan health insurance, 50% Navigo.
