Doctolib
Healthcare
PrincipalMachineLearningEngineerAppliedAI&LLMs(x/f/m)
“Principal Machine Learning Engineer - Applied AI & LLMs (x/f/m) at Doctolib. Skills: Machine Learning, Applied AI, LLMs, Python. Own cross-team ML architecture decisions. Define and drive adoption of evaluation frameworks”
What You'll Achieve.
Measurably improve outcomes for care teams and patients; Directly impacting the daily lives of 400,000 healthcare professionals; Directly impacting the health of 80 million patients; Meet EU regulatory expectations
Industry & Context.
Own cross-cutting technical challenges; Take on deep technical work
Work from abroad for up to 10 days per year, Relocation support for international mobility
What They're Looking For.
Must Have
10+ years in ML/AI, 3+ years at Staff+ or Principal level leading complex, multi-team technical initiatives, Deep expertise in at least two of: clinical NLP, LLM fine-tuning and evaluation, automatic speech recognition, RAG systems, or reinforcement learning, Expert in Python, PyTorch/Transformers for training, and vLLM for inference, Track record deploying ML systems in production (AWS/GCP), PhD in Computer Science, AI, Statistics, or related field (or equivalent research experience), Exceptional communication skills
Nice to Have
Publications in top-tier ML/AI conferences (NeurIPS, ICLR, ACL) or medical informatics venues, Experience with EU healthcare regulations (GDPR, MDR, AI Act), Prior work with clinical data or healthcare applications
What You'll Do.
Own cross-team ML architecture decisions
Define and drive adoption of evaluation frameworks
Establish shared production infrastructure patterns
Lead technical design of agentic solutions
Elevate technical standards across DS & AI org
How You'll Work.
Team & Collaboration
Work across all AI teams: Clinical, Productivity, Phone Assistant and ASR; Influence Staff and Senior ML Engineers; Partner with Product, Legal, and Compliance teams; Align diverse stakeholders
Communication Scope
Exceptional communication skills; Explain complex technical decisions
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