We are looking for a Staff Machine Learning Engineer - Retrieval to join our AI team working on Clinical decision support and Medical Knowledge. Your mission will be to ground clinical AI decisions in validated, trusted medical sources from HAS guidelines and learned society recommendations to peer-reviewed clinical studies. You will work in a feature team developing Doctolib's Medical Knowledge Platform, contributing directly to the reliability and safety of AI-powered healthcare experiences used by hundreds of thousands of health professionals and millions of patients across Europe.
🏢 About Doctolib
Working in the tech team at Doctolib means building innovative products and features to improve the daily lives of care teams and patients.
🎯 The Role
Your mission will be to ground clinical AI decisions in validated, trusted medical sources from HAS guidelines and learned society recommendations to peer-reviewed clinical studies. You will work in a feature team developing Doctolib's Medical Knowledge Platform, contributing directly to the reliability and safety of AI-powered healthcare experiences used by hundreds of thousands of health professionals and millions of patients across Europe.
✅ Key Responsibilities
Build and own Doctolib's Medical Knowledge Platform, designing the systems that ground clinical AI decisions in validated medical sources (HAS guidelines, learned society recommendations, clinical studies)
Design and maintain both indexing pipelines and retrieval systems, ensuring end-to-end ownership across the full retrieval stack
Operate at scale: architect and optimize systems handling 100M+ documents, 50+ req/s, and sub-300ms latency requirements
Engineer deep RAG systems from the ground up - going well beyond off-the-shelf components to tackle real medical knowledge complexity
Work across the full retrieval stack: vector embeddings, vector search, re-ranking, query expansion and rewriting
📌 Required Qualifications
Have proven production experience building and scaling search & retrieval systems in a real-world, high-traffic environment
Bring deep dual expertise in both offline indexing and online retrieval; you own the full pipeline, not just one side of it.
Are genuinely hands-on with the retrieval tech stack: Elasticsearch, Solr, Vertex AI, vector search, embeddings, re-ranking and query processing.
Have gone beyond commodity RAG; you've built custom re-rankers, optimized indexing pipelines, or engineered query processing solutions that required real technical depth.
Have operated at Senior or Staff level, with the autonomy and technical depth that comes with it.
⭐ Desirable Experience
Have experience working with medical knowledge sources, clinical databases, or healthcare information systems
Have contributed to evaluation frameworks and benchmarks for retrieval quality or AI outputs in regulated or high-stakes environments
Have worked in a multi-lingual retrieval context or with domain-specific ontologies
🎁 Benefits
Free comprehensive health insurance (basic package) for you and your children
25 days of paid vacation per year, plus up to 14 days of RTT
Free mental health and coaching services through our partner Moka.care
Work from abroad for up to 10 days per year thanks to our flexibility days policy
Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib
A subsidy from the work council to refund part of the membership to a sport club or a creative class
50% reimbursement of your public transport subscription
ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave and 75% during the second, covered by Doctolib
Enrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth
For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support
Relocation support in case of international mobility
Access to the best AI tools for coding, development and dedicated training
🛂 Visa & Eligibility
Relocation support in case of international mobility
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