Doctolib
Healthcare
SeniorMachineLearningEngineer-AppliedAI&LLMs(x/f/m)
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optimal for Senior candidates.
“Senior Machine Learning Engineer - Applied AI & LLMs (x/f/m) at Doctolib. Skills: Machine Learning, AI Engineering, LLM, Agentic AI. Design ML/AI solutions. Build retrieval pipelines”
What You'll Achieve.
Improve access to care; Manage health over time; Create measurable impact; Scale AI capabilities
Industry & Context.
Analytical skills
What They're Looking For.
Must Have
7+ years experience ML/AI, Experience Information Retrieval, Proficient LLM/VLM application development, Hands-on experience agentic AI systems, Scientific rigor, Experience operating large-scale applications, Fluent in English
Nice to Have
Experience B2C marketplace, Experience pattern mining, Experience recommendation systems, Experience experimentation, Experience causal inference
What You'll Do.
Design ML/AI solutions
Build retrieval pipelines
Maintain retrieval pipelines
Develop LLM/VLM models
Fine-tune LLM/VLM models
Evaluate LLM/VLM models
Build agentic AI systems
Integrate external data
Deploy solutions to production
Act as technical reference
How You'll Work.
Team & Collaboration
Feature team; ML platform team
Communication Scope
Communicate findings
Full Job Description
Your Impact 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. Working in the tech team at Doctolib means building innovative products and features to improve the daily lives of care teams and patients. What you'll do Your responsibilities include but are not limited to: 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 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'll be a great fit if you: You have 7+ years of experience in Machine Learning, Deep Learning, or AI Eng
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