AI Talent Pool

Role Summary

Are you passionate about artificial intelligence and want to contribute to revolutionizing the healthcare sector? Doctolib develops cutting-edge AI solutions to improve access to care and facilitate the daily lives of healthcare professionals and patients. Our AI/ML teams work on large-scale projects: medical natural language processing, recommendation systems, clinical process automation, and much more.

We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision here !

By joining our AI talent pool, you will be contacted as a priority for our future opportunities in the following areas:

The areas of expertise we are looking for

• ML Engineering & MLops: Python proficiency, Model deployment, Evaluation frameworks (Offline/Online) & Model monitoring
• Natural Language Process (NLP): Clinical NLP, LLM expertise Deep Learning or Speech / Language modeling
• LLMops & Generative AI: Prompt Engineering, RAG, LLM fine-tuning, Embedding models, Agent frameworks
• Automatic Speech Recognition (ASR): Voice Activity Detection, Interactive voice assistants, audio processing, Speech-to-text
• Search, Ranking, information retrieval
• Reasoning capabilities: Logical reasoning, Multi-step reasoning, Chain-of-thought approaches
• Knowledge representation: Knowledge graphs, ontologies, Semantic modeling
• Reinforcement Learning: RL algorithms, Policy optimization, Reward modeling

Requirements

Most of our positions require

Before reading further, if you don't have the exact profile described below, but you think this job description matches your skills and aspirations, we still encourage you to apply.

• Hold a degree in computer science, applied mathematics, data science, or related field (Master's, PhD, or equivalent)
• Master machine learning and deep learning frameworks (PyTorch, TensorFlow, Scikit-learn)
• Have hands-on experience with LLMs, open and closed source Proficient in Python
• Possess strong analytical skills and a results-oriented approach
• You speak English fluently (international work environment)

It would be fantastic if

• You have experience in healthcare or medical NLP
• You master RAG techniques, embeddings, and vector search
• You know MLOps technologies and model deployment in production (Kubernetes, Docker, MLflow)
• You have contributed to open-source projects or published in AI conferences

At Doctolib, we believe in improving access to healthcare for everyone - regardless of where you come from, what you look like. This translates into our recruitment process: Doctolib is an equal opportunity employer. We don't just accept diversity at Doctolib, we respect and celebrate it!

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, or place of origin. If you have a disability, let us know if there's any way we can make the interview process smoother for you!

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About the Company

WHAT WE DO

Join a team of passionate and hardworking entrepreneurs to transform healthcare!

Working in the tech team at Doctolib involves building innovative products and features to improve the daily lives of care teams and patients. We work in feature teams in an agile environment, while collaborating with product, design, and business teams.

ABOUT OUR TECH ENVIRONMENT

Our solutions are built on a single, fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to the country and healthcare specialty requirements. To address these challenges, we are modularizing our platform run in a distributed architecture through reusable components.

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 https://careers.doctolib.com/ai-careers/!

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 https://huggingface.co/blog/hugging-science/doctobert-cookbook#introduction to learn more about how we bring deep AI research into practical healthcare applications.