AI Engineer

• Design and build the production search & recommendation architecture: full retrieval, ranking, reranking pipeline with standard and off-the-shelf components (vector search, semantic retrieval, LLM/managed rerankers).
• Establish strong baselines first (prompts, RAG, model selection) before reaching for custom ML.
• Build evaluation and observability into every stage, with offline and online evaluation.
• Set up the data/event feedback loops that drive iteration and feed deeper ML later.
• Improve search relevance and ranking on Patient facing products , raising result quality
• Own production quality: latency reliability, monitoring, and maintainability.
• Partner with ML Engineers and collaborate closely with PMs and SWEs to define, build, and ship AI-powered features that deliver measurable value to users and the business., * Our solutions are built on a single, fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to country and healthcare specialty requirements.
• Our stack is composed of Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native.
• We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision here.
• We also invest in open, applied AI research. For example, our DoctoBERT project introduces open-source medical language models trained for French clinical text, with applications in named entity recognition, classification, and retrieval. Read the DoctoBERT practical guide to learn more about how we bring deep AI research into practical healthcare applications.

1. Production deployment: ability to ship algorithms to production (ECS-based service on AWS)
2. Strong analytical mindset: result-oriented, patient-first approach
3. Significant experience as a Software or/and AI engineer shipping search or recommendation systems to production.
4. Hands-on experience building end-to-end retrieval: ranking, reranking pipelines and familiar with nDCG, MAP, Recall@k, MRR
5. AI-engineering proficiency: turning foundation models and off-the-shelf components into production systems: embeddings & vector search, semantic retrieval, RAG, LLM-based or managed rerankers (e.g. Vertex AI). You can succeed without training a model from scratch
6. Architecture-first approach: you build the system, baselines, evals, and feedback loops with standard tooling before reaching for custom ML, and know when to partner with ML Engineers to break a ceiling
7. Evaluation & observability built into every stage (retrieval, ranker, reranker) - offline and online eval, A/B testing, position-bias handling, monitoring
8. Production deployment - ability to ship reliable, low-latency services to production (hundreds-of-ms SLAs), with care for data quality and long-term maintainability

Now, it would be fantastic if you:

• Experience at a B2C marketplace (e-commerce, hospitality, travel)
• Additional ML methodologies: pattern mining, recommendation systems, experimentation, or causal inference
• Have experience with search engines or information retrieval concepts
• Have exposure to learning-to-rank or feature engineering (for breaking ceilings later, alongside ML Engineers)
• Have experience in a healthcare or other regulated domain (GDPR / HDS)

• 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
• Enrollment in Doctolib’s long-term employee value sharing plan called DoctoGrowth
• ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave, and 75% during the second, covered by Doctolib
• For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support

The interview process

• A first interview with a member of the Talent Acquisition team
• A coding interview
• An agentics system design
• A behavioural interview
• Reference check

The Patient domain sits at the heart of Doctolib’s mission: ensuring everyone has better access to the care they need, receives better care from health professionals, and can actively prevent health problems to improve their wellbeing.

You’ll design the search and recommendation engines behind our health companion, helping 100M patients across Europe instantly navigate to the exact care they need while delivering trusted, curated insights at every step. The retrieval and recommendation architecture you own will directly shape how relevant, fast, and trustworthy that experience is for every one of them.