Senior Machine Learning Engineer - Applied AI & LLMs

We are looking for a Senior Machine Learning Engineer to join the ML Engineering team in Patient Solutions. Your mission will be to improve how people access quality care and manage their health over time by building and leading AI and ML systems that create real, measurable impact. You will work in a feature team developing intelligent patient-facing solutions, from smart practitioner discovery to long-term care management, playing a key technical role in shaping how we scale our AI capabilities across Europe.

Responsibilities

Design and implement ML and AI solutions aligned with patient product goals, covering search, retrieval, and personalized care pathways; Build and maintain large-scale retrieval pipelines, including hybrid search, embedding systems, vector databases, and multi-stage re-ranking architectures; Develop, fine-tune, and evaluate LLM and VLM models using techniques such as knowledge distillation, Mixture-of-Experts (MoE) architectures, and prompt engineering; Build and orchestrate agentic AI systems, integrating external data and capabilities through tools and MCP-based integrations; Define metrics aligned with product goals, run controlled end-to-end experiments using W&B, MLFlow, or Braintrust, and communicate findings to guide product and technical decisions; Deploy solutions to production in collaboration with our ML platform team, ensuring reliability, observability, and performance at scale, and act as a technical reference to elevate the team's standards and practices

Requirements

7+ years of experience in Machine Learning, Deep Learning, or AI Engineering, with a strong track record of taking models from prototype to production at scale; strong experience in Information Retrieval and modern retrieval stacks: hybrid search (sparse + dense), large-scale embeddings and vector databases, multi-stage retrieval and re-ranking pipelines, RAG architectures, and tool/MCP-based integrations; proficient in LLM and VLM application development: fine-tuning, MoE architectures (via LiteLLM or Model Garden), knowledge distillation, prompt engineering, and systematic benchmarking of LLM/VLM systems; hands-on experience building and orchestrating agentic AI systems (e.g., using ADK); strong scientific rigor: designing metrics aligned with product goals, running controlled experiments, and communicating results clearly to both product and engineering stakeholders; experience operating large-scale applications in production (monitoring, reliability, performance, observability), strong analytical skills, user-first mindset, fluent in English

Nice to have

Experience in B2C marketplace environments; experience in other ML methodologies: pattern mining, recommendation systems, experimentation, or causal inference

Benefits

Free comprehensive health insurance (basic package) for you and your children; 25 days of paid vacation per year, plus up to 14 days of RTT; Free mental health and coaching services through our partner Moka.care; Work from abroad for up to 10 days per year thanks to our flexibility days policy; Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib; A subsidy from the work council to refund part of the membership to a sport club or a creative class; 50% reimbursement of your public transport subscription; ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave, and 75% during the second, covered by Doctolib; 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; Access to the best AI tools for coding, development and dedicated training