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

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
• 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), with care for data quality and long-term maintainability
• 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)
• 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.
• Want to learn more about our tech culture and environment? Visit the Doctolib Tech site.