{"id":66003,"url":"https://alion.io/job/doctolib-research-scientist-xfm","title":"Research Scientist (x/f/m)","company":{"id":52886,"name":"Doctolib","domain":"doctolib.fr","url":"https://alion.io/company/doctolib","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":83,"open_postings":63,"ghost_share":0.016,"stale_share":0.619,"repost_share":0.048,"time_to_fill_p50_days":34,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Paris, France"],"countries":["FR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":58000,"max_usd":126000,"period":"year","method":"role_country_seniority_unknown","sample_n":121},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"World Models","optional":false},{"name":"AI Agents","optional":true},{"name":"Interpretability","optional":true},{"name":"Java","optional":true},{"name":"JavaScript","optional":true},{"name":"Kotlin","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"React Native","optional":true},{"name":"React.js","optional":true},{"name":"Self-Supervised Learning","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-08-06T15:21:25Z","employer_posted_date":"2026-08-06","last_verified_at":"2026-10-31T11:05:37Z","board_verified":true,"closed_at":null,"days_open":85,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":85},"description":"Set a new pulse for healthcare!\nWe are looking for a Research Scientist to join the Doctolab team (Doctolib Clinical AI Research Lab).\nYour mission is to build accurate, well-calibrated, and clinically reliable models of patient health, learned from health data at scale. The work is both fundamental and applied: you publish, and your models reach products used by doctors and patients. Doctolib is used by around 450,000 health professionals and 90 million people across Europe, and research that succeeds in the lab can be deployed at that scale.\nWorking at Doctolib means contributing to one of Europe's leading health-tech companies, and seeing your work improve care for patients and practitioners.\nHow we work\nDoctolab is a research lab in its founding phase, so researchers have real influence over its direction and its priorities. We work closely with the data science and product teams, and we stay close to the data.\nWe expect researchers to explain why a question matters, not only why it is open. Projects are chosen on both scientific ambition and what they change for patients and practitioners. The work suits researchers who want to see their results used.\nThe questions we raise are open problems in machine learning: world models that predict the consequences of an action, calibration and uncertainty, causal inference from observational data, multimodal sequence modelling, orchestration between model capabilities. Results on these questions hold well beyond healthcare. We are in a strong position to work on them: we have the data to learn a world model from, a clinical use case that defines success, and the engineering path to put it in front of practitioners and patients and learn from what comes back.\nExperience with healthcare or medical data is a plus but not required. Both early-career and experienced researchers are welcome.\nWhat you'll do\nYour responsibilities include but are not limited to:\nBuild a world model of patient health: how a health state evolves over time and changes in response to care, learned from observational data with the confounding accounted for\n\nBuild evaluation methods that test calibration, robustness, interpretability, privacy, and generalization outside the training distribution\n\nWork with the data science and product teams to turn research results into clinical-grade features\n\nPublish at leading machine learning and health informatics venues\n\nEngage the international health and machine learning community through open collaboration: data challenges, shared benchmarks, and open-source releases\n\nHelp define the lab's research agenda and its working relationship with the rest of the company\n\nWho you are\nBefore 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.\nYou'll be a great fit if you:\nHave a PhD in machine learning, statistics, computer science, or a related field, or a Master's degree with significant research experience\n\nHave depth in one or more of: representation learning and self-supervised learning; large language models and multimodal modelling; causal inference and causal discovery from observational data; temporal and dynamic modelling; calibration and uncertainty quantification; agentic systems and orchestration; evaluation and benchmarking; privacy auditing of machine learning models\n\nHave first-author publications at leading venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, or comparable medical informatics venues\n\nHave driven your own research, from choosing the question to publishing the result\n\nAre proficient in Python and a deep learning framework such as PyTorch, and have trained models yourself\n\nAre comfortable working with large and imperfect real-world data\n\nCan explain why a research question matters, scientifically and for patients or practitioners\n\nAre fluent in English, the working language of the lab\n\nIt would be fantastic if you:\nHave experience with healthcare or medical data, clinical text, or electronic health records\n\nHave taken research models into production, or worked alongside product teams\n\nHave released open-source code, benchmarks, or datasets\n\nLife at Doctolib Tech\nOur 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.\n\nOur stack is composed of Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native.\n\nWe leverage AI ethically across our products to empower patients and health professionals. Discover our AI visionhere.\n\nWant to learn more about our tech culture and environment? Visit the Doctolib Tech site.\nWhat we offer\nFree comprehensive health insurance (basic package) for you and your children\n\n25 days of paid vacation per year, plus up to 14 days of RTT\n\nFree mental health and coaching services through our partner Moka.care\n\nWork from abroad for up to 10 days per year thanks to our flexibility days policy\n\nLunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib\n\nA subsidy from the work council to refund part of the membership to a sport club or a creative class\n\n50% reimbursement of your public transport subscription\n\nParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave and 75% during the second, covered by Doctolib\n\nEnrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth\n\nFor caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support\n\nRelocation support in case of international mobility\n\nAccess to the best AI tools for coding, development and dedicated training\n\nOur interview process\nRecruiter Interview\n\nOne or more Research Background Interviews\n\nTechnical / Problem-Solving Interview\n\nBehavioral Interview\n\nAt least one reference check\n\nWe want your experience to be clear, respectful, and transparent. Learn more about our hiring process on our candidate experience page.\nJob details\nPermanent position\n\nFull-time\n\nParis, France\n\nHybrid work setup (up to 2 remote days per week)\n\nStart date: as soon as possible\n\nWe welcome everyone\nAt 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.\nThe 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.\nTo 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.\nJoin us in building the healthcare we all dream of!\nYour data privacy\nAll information provided is processed by Doctolib for application management. For data processing details, click France. 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