Lead ML Engineer

We're Ooak Data

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 8 figures contracts signed with frontier AI labs, and we are scaling delivery aggressively over the next months.

🤝 Our values

• Trust, the foundation of every relationship, internal and external. We extend it by default and value the ownership that comes with it.
• Ambition, we commit, we move fast, with tenacity and efficiency.
• Collective, team first, low ego.
• Kindness, at the heart of every interaction.

The offer

📍 Location: Paris (city center), on-site with 1 to 2 days WFH

🕐 Start: as soon as you are available

💼 Contract: full-time (CDI)

💰 Compensation: €90k to €120k + bonus + equity (BSPCE)

✅ Your missions

As Lead ML Engineer, you build and lead our ML team. You report to Thomas (CTO & co-founder).

Our ML team is central: it works with every product team on the hardest parts of the pipeline that turns full company data ecosystems into anonymized digital twins and training environments for the labs.

• Own ML across the pipeline: detection of sensitive entities in text and images, entity resolution and the knowledge graph that keeps every pseudonym consistent across all tools, and document understanding to enrich our datasets.
• Automate quality: automated evaluation of anonymization and dataset quality, so delivery can scale without adding manual checks.
• Lead and grow the team: a few ML engineers today, around 6 by mid 2027. Priorities, 1:1s, hiring, and the technical bar.
• Stay hands-on: you prototype, code and ship to production yourself.
• Prepare what comes next with Thomas: RL environments and tasks built on our data, and fine-tuning open-source models on company data.
• Keep a close eye on research: read the papers, know where the frontier labs are heading, and turn it into our roadmap.

Your first months: you start embedded in one of our product teams for 1 to 3 months to learn the pipeline from the inside, then you take the lead of the ML team.

🔍 Who we're looking for

• 8+ years in machine learning engineering, including 2+ years leading ML engineers.
• Deep hands-on expertise: Python, PyTorch or JAX with NLP and LLMs. Computer vision is a plus.
• Production ML at scale: you have shipped models to production and owned them: evaluation, monitoring, versioning, experiment tracking, GPU infrastructure (GCP or equivalent).
• LLMs in practice: fine-tuning, inference (vLLM, SGLang or equivalent) and evaluation.
• AI-native way of building: you use coding agents every day and you know how to make a team ship faster with them.
• Research-literate: you read papers and turn them into things that ship.
• High bar on quality and security: we handle our partners' most sensitive data.
• Team first, low ego: you lead by clarity and trust, and you make the people around you better.
• Fluent English, mandatory (working language with our clients and the labs).

The extras that make the difference

• Experience with reinforcement learning: environments, reward design, RL frameworks.
• Experience with entity resolution or knowledge graphs.
• Experience with anonymization or privacy-preserving ML.
• Experience at an AI lab .

🎁 Why join us

• Entrepreneurial adventure: founding impact, live the early days of an ambitious company at the front line of the AI revolution.
• Backed by Y Combinator, join just as we accelerate.
• Offices in central Paris.
• Perks: Alan health insurance (mutuelle), 50% Navigo covered.

✏️ Recruitment process

We move fast.

• A 30 min intro call with Grégoire (co-founder)
• A 60 min technical deep dive with Thomas (CTO & co-founder): your track record, technical judgment, how you lead
• A technical ML case, presented to Thomas and an ML engineer
• A final fit interview with 2 founders
• Reference calls