ФранцияLead ML Engineer
3 дн. назад
ФранцияLeadHybrid
reinforcement learningmachine learningdata processing
Lead ML Engineer to build and lead the ML team at , working on enterprise data for AI reinforcement learning environments.
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 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.
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.
- Offices in central Paris.
- Perks : Alan health insurance (mutuelle), 50% Navigo covered.
Как откликнуться
- 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
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 delivery 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: €90k to €120k + bonus + equity (BSPCE)
- 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).
- 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 .