Paris, Paris, France
Open 3 months, first seen by us 100 days agoJob details Permanent position Tech stack: Python, Elasticsearch, Solr, Vertex AI, vector search, embeddings
Tech stack: Python, Elasticsearch, Solr, Vertex AI, vector search, embeddings Full-time Levallois-Perret, France
Employment type in the employer’s posting: Unlimited ContractFrom the employer’s posting
Build and own the Medical Knowledge Platform that grounds clinical AI in validated medical sources
Design and maintain indexing pipelines and retrieval systems end to end
Architect and optimize systems for 100M+ documents, 50+ req/s, sub-300ms latency
Build custom re-rankers and optimize query processing pipelines
Engineer deep RAG systems beyond off-the-shelf components
Build and own Doctolib's Medical Knowledge Platform
Design and maintain both indexing pipelines and retrieval systems, ensuring end-to-end ownership across the full retrieval stack
architect and optimize systems handling 100M+ documents, 50+ req/s, and sub-300ms latency requirements
Build custom re-rankers, optimize query processing pipelines, and design robust retrieval evaluation frameworks
Engineer deep RAG systems from the ground up - going well beyond off-the-shelf components
From the employer’s posting.
Open 3 monthsWe first saw this posting 100 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 100
Up longer than most. Similar jobs usually come down between day 6 and day 70.
Well past Doctolib’s usual: its jobs come down by about day 15.
From 4,442 similar postings that came down, and 14 of Doctolib’s. Coming down is not always a hire.
10 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- 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.
- Permanent position - Tech stack: Python, Elasticsearch, Solr, Vertex AI, vector search, embeddings - Full-time
- 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.
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Already applied? Track this application
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
Working in the tech team at Doctolib means building innovative products and features to improve the daily lives of care teams and patients.
Your responsibilities include but are not limited to:
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'll be a great fit if you:
It would be fantastic if you:
Want to learn more about our tech culture and environment? Visit the Doctolib Tech site.
We want your experience to be clear, respectful, and transparent. Learn more about our hiring process on our candidate experience page.
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, or 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!
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Paris, Paris, France
- 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
- Levallois-Perret, France - Hybrid work setup (up to 2 remote days per week) - Start date: as soon as possible
No clear work-authorization passage found. Eligibility is unconfirmed.