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

Doctolib

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  1. Why do you want to join Doctolib?
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  3. When would you not use machine learning for a problem?
  4. Walk me through a model you built, from the data to how it was used.
  5. How did you know your model was actually good, and not just good on your test set?

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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:


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:

  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:

Life at Doctolib Tech

Want to learn more about our tech culture and environment? Visit the Doctolib Tech site.

What we offer

 

The interview process 

 

Job details

 

 

At Doctolib, we are committed to improving access to healthcare for everyone. This translates into our recruitment process. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination.

The more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply to Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, disability.

To ensure equal opportunities, we invite you to exclude personal information (e.g. pictures, age) from your applications. If you require any accommodation, please let us know for support during the hiring process. 

Join us in building the healthcare we all dream of!

All information provided is processed by Doctolib for application management. For data processing details, click here: Germany l France l Italy l Netherlands. Please contact hr.dataprivacy(at)doctolib.com to exercise your rights.

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Listed on arbeitnow · posted 2026-10-23. ApplySarthi collects openings and links to application pages; the role is advertised by Doctolib, not by us.