Product ML Engineer

Responsibilities: — Embed directly within a product team. — Collaborate closely with SMB, Scaler or Enterprise teams on focused ML problems tied to clear customer and business outcomes. — Own ML projects end to end: frame the problem, choose the approach, execute the plan, ship the feature and monitor its performance in production. — Make build vs. buy decisions, determining when to fine-tune or build a model yourself versus integrating an external model, API or existing library. — Move from idea to production quickly, working on projects scoped in weeks rather than quarters, prioritising rapid iteration and customer impact. — Work closely with the ML team, leveraging shared infrastructure, evaluation tooling and expertise while remaining embedded in the product team's priorities and cadence. — Stay close to users by getting rapid feedback, monitoring usage, and using insights to drive the next iteration. — Balance technical depth with pragmatism, knowing when deeper modelling work is necessary and when an existing solution is sufficient. — Write production code directly in the product team's codebase rather than primarily through research notebooks and experiments. — Own quality in production by building sound evaluations, monitoring performance and investigating failures once the feature is live. Requirements: — 2+ years of hands-on experience building and shipping ML-powered products. — Strong experience with PyTorch or equivalent frameworks such as TensorFlow or JAX. — Applied-science depth, able to fine-tune vision or diffusion models, design meaningful evaluations and debug training runs. — Pragmatic builder focused on solving customer problems rather than proving model superiority. — Product-minded, measuring success through shipped features, user behaviour and business outcomes. — Comfortable working in production codebases and moving between experimentation and production engineering. — Strong ownership and ability to take ambiguous ML problems through to production. — Enjoys short feedback loops and rapid iteration. — Curious, collaborative, values knowledge sharing and humility. — Fluent in English; French is not required. Nice to have: — Experience shipping generative image models or diffusion models into production. — Experience evaluating third-party AI APIs against internal alternatives. — Experience working in a fast-growing startup or directly within a product team. Benefits: — Competitive salary plus meaningful equity. — Hybrid working from Paris headquarters. — Visa sponsorship and relocation support up to €10,000. — MacBook Pro. — Private healthcare. — Regular company off-sites and team retreats. — International English-speaking team. — Language lessons in English and French. Hiring Process: — Screening with an ML Researcher / Engineer. — Home technical assignment and review with the team. — Culture fit interview and meet the team. — Reference check and offer.