Senior AI Engineer - Patient Team (x/f/m)

Join our mission, join Doctolib!  We are looking for a Senior AI Engineer to join the Patient team in Paris.  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. Your responsibilities include but are not limited to: 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.  Who you are Before you read on:  if you don't have the exact profile described below, but you feel this job description matches your skill set, we still encourage you to apply. You could be our next team mate if you have: Production deployment: ability to ship algorithms to production (ECS-based service on AWS) Strong analytical mindset: result-oriented,  patient-first approach Significant experience as a Software or/and AI engineer shipping search or recommendation systems to production.  Hands-on experience building end-to-end retrieval: ranking,  reranking pipelines and familiar with nDCG, MAP, Recall@k, MRR 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 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 Evaluation & observability built into every stage (retrieval, ranker, reranker) — offline and online eval, A/B testing, position-bias handling, monitoring Production deployment — ability to ship reliable, low-latency services to production (hundreds-of-ms SLAs), w...