Senior Staff Machine Learning Engineer - Medical Practice Efficiency

As a Senior Staff Machine Learning Engineer at Medical Practice Efficiency team, your mission will be to lead the technical direction for AI systems that streamline healthcare operations, helping healthcare professionals save time and focus on patient care. Including agentic phone assistants, ML/LLM-based scheduling optimization, automated billing, and personal assistants that support healthcare professionals in using Doctolib software.

You'll design and ship production Agentic/LLM/ML systems (and associated evaluation) that handle millions of healthcare interactions daily, balancing innovation with the operational rigor required in healthcare. This role emphasizes AI production excellence: building robust, reliable models and establishing quality standards that enable safe, rapid deployment.

Your responsibilities include but are not limited to:

Technical Strategy & Platform

• Own the long-term technical roadmap for medical practice efficiency systems
• Drive build vs. buy decisions for model capabilities, balancing custom development with foundation model APIs
• Define patterns for production deployment: evaluation standards, monitoring, A/B testing, and safe rollout in healthcare contexts

End-to-End ML Delivery

• Lead design and implementation of high-impact models: LLM-powered phone/chat assistants, billing automation, intelligent search and ranking, appointment optimization
• Ship production models with robust evaluation, monitoring, drift detection, and incident response
• Establish rigorous offline/online metrics and experimentation frameworks that measure real business impact and clinical safety

Leadership & Responsible AI

• Mentor Staff/Senior ML Engineers, setting standards for model quality, evaluation rigor, and production best practices
• Partner with Product, Legal, and Compliance to ensure models meet privacy, security, and regulatory requirements G eneral Data Protection Regulation (GDPR), data minimization, bias testing)
• Lead cross-functional initiatives from discovery to production, influencing roadmaps and unblocking technical execution

• You have 10+ years in ML/AI/large-scale systems with 3+ years at Staff+ or Principal level leading complex, cross-team initiatives
• You have deep expertise in at least two of: production NLP systems (entity recognition, classification, information extraction), LLM evaluation and fine-tuning for production use cases, search/ranking/recommendation systems, or trust & safety / anomaly detection
• You are expert in Python, PyTorch/Transformers, and modern ML infrastructure (feature stores, model serving, monitoring)
• You have production experience with cloud platforms (AWS/GCP) and strong understanding of service level agreements (SLAs), cost optimization, and operational excellence
• You have exceptional communication and cross-functional leadership skills

Now it would be fantastic if you have:

• PhD in Computer Science, AI, Statistics, or related field (or equivalent research experience)
• Experience with LLM guardrails, retrieval-augmented generation (RAG) systems, and cost/performance optimization for production deployments
• Prior work in regulated environments (healthcare, finance) or privacy-sensitive domains, Familiarity with General Data Protection Regulation (GDPR), data governance best practices, and EU healthcare regulations

• Free mental health and coaching services through our partner Moka.care
• For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support
• Work from EU countries and the UK for up to 10 days per year, thanks to our flexibility days policy
• Work Council subsidy to refund part of sport club membership or creative class
• Up to 14 days of RTT
• A subsidy from the work council to refund part of the membership to a sport club or a creative class
• Lunch voucher with Swile card

The interview process

• HR Screen
• Hiring Manager Interview
• Behavioral Interview
• Technical Questions
• Technical Case Study
• At least one reference check