ФранцияHead of Operations
1 нед. назад
80k–100k EUR / годФранцияHeadHybrid
Lead and manage the operations team responsible for delivery processes between signed partners and dataset shipments to frontier AI labs.
О компании
- 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.
Обязанности
- As Head of Operations , you own the delivery machine: everything that happens between a signed partner and a dataset shipped to a frontier lab. You'll report directly to Grégoire (COO & co-founder) and manage the Ops team.
- Your perimeter is the full chain: evaluation → ingestion → anonymization → enrichment → delivery .
- Own delivery end to end. Timelines, quality, and throughput across every dataset we ship.
- Manage and grow the Ops team. You inherit 3 to 4 Ops Managers who already know the machine and are performing. Your job is to make them grow and thrive.
- Own data quality. The QA layer and the annotator workforce sit in your org. You set the quality bar, the way it is measured, and the way it is enforced.
- Industrialize. Much of the chain still takes manual jobs. You redesign it, instrument it, and automate it with the eng and product teams.
- Work with the founders on delivery strategy, capacity planning, and what we can credibly commit to our clients.
- A step change in delivery speed , with the time between a signed partner and a shipped dataset cut dramatically.
- A much lighter cost per delivery : same output, a fraction of the human hours.
- A team that scales without you in the loop : clear ownership & metrics everyone can read.
Условия
- 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.
- A team already in place : you are not starting from zero, you are taking a working machine and making it several times bigger.
- Offices in central Paris.
- Perks : Alan health insurance (mutuelle), 50% Navigo covered.
Другое
- 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 aggressively over the next months.
- 📍 Location: Paris (city center), on-site with 1 to 2 days WFH
- 🕐 Start: as soon as you are available
- 💼 Contract: full-time (CDI)
- 💰 Compensation: fixed salary + equity (BSPCE)
- 8+ years in operations , delivery, program management, consulting, or a fast-moving startup, with real ownership of a production chain.
- 3+ years managing a team , including senior ICs. This is mandatory.
- A true ops mindset : you think in processes, bottlenecks, cycle times, and unit economics. You measure before you decide.
- An obsession with quality. You have built quality systems, not just quality checks, and you know the difference.
- Deep curiosity , the kind that makes you dig until you actually understand how something works, including the technical parts.
- Serious AI literacy : you understand how models are trained, what RL and post-training are for, and why the data we produce matters. You use AI daily as leverage, not as a toy.
- Automation reflex : you spot repetitive work and turn it into a pipeline. You can script, or vibe-code, or you are hungry to learn fast.
- Fluent English , mandatory (working language with our partners and the labs).
- Experience scaling a data operations or annotation organization .
- Experience working with data or ML engineers , and enough technical vocabulary to arbitrate with them.
- Exposure to privacy, anonymization, or security-sensitive data handling .