Preferred Experience

4+ years in data quality, data operations, or QA, ideally at a data labeling company, a data provider, or an AI company.
Hands-on with large, messy datasets: processing them, auditing them, finding what is wrong with them. Python and SQL are required: you write your own scripts to interrogate a dataset, you do not wait for someone to pull the data for you.
Comfortable in a tech team. You will sit with engineers, read their pipeline, and hold your own in a technical conversation. You are not expected to ship production code, but you are expected to be credible.
A track record of building quality standards from scratch, not just applying someone else's checklist.
Obsessive attention to detail. You enjoy finding the error everyone else missed, and you are not satisfied until you understand why it was there.
Systems thinking. When you find one error, your instinct is to ask how many others like it exist and how to catch them automatically.
AI-first. You use LLMs and AI tools as daily leverage to audit, automate, and move faster.
Fluent English, mandatory (working language with our partners and the labs).

The extras that make the difference

Familiarity with PII, de-identification, or privacy requirements.
Exposure to annotation quality concepts: rubrics, inter-annotator agreement, guideline design.
Understanding of how training data is actually used: SFT, RLHF, RL environments, evaluation.
Experience working with external vendors or an annotation workforce.

A function to build, not to inherit. Quality at Ooak Data is yours to define from the ground up.
Your work is visible. What you validate goes straight to the largest AI labs in the world.
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 45 min technical deep dive with Thomas (CTO & co-founder): how you work with data, what you have built, how you think about detection
A technical case: auditing a real anonymized dataset, debriefed with Grégoire and Thomas
A final fit interview with 2 founders