ML Ops

Set a new pulse for healthcare!

We are looking for a Senior MLOps Engineer to join the ML Platform (TIGER) team in Artificial Intelligence & Machine Learning.

Your mission will be to power every ML and AI initiative at Doctolib — from experiment to reliable production service. You will build and operate the foundations that let data scientists, ML engineers, and AI teams ship models and agents safely, fast, and at scale, from classic machine learning to self-hosted generative AI. You will contribute directly to the reliability and performance of AI-powered features that improve the daily lives of millions of patients and thousands of healthcare professionals 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.

• Partner with data scientists, ML & AI engineers to take models from experimentation to reliable, scalable production deployment.
• Operate and evolve the orchestration layer for training, serving and evaluation workloads, built on GCP and AWS using Kubernetes (GKE) and Ray-based platforms (Anyscale) — including CI/CD, versioning and automated testing pipelines that carry a model from the registry to a served endpoint.
• Build the tools, templates and best practices (Terraform modules, Kubernetes CRDs and operators, GitOps workflows, internal libraries) that let any ML team deploy and operate their models and agents safely and consistently.
• Ensure the availability, reliability and performance of ML systems in production across training, serving and the agents platform; define and track SLOs; take part in on-call and incident response for the whole stack.
• Build and maintain observability for ML systems: infrastructure and model health via Datadog, and evaluation/quality signals via Braintrust, catching regressions before they reach users.
• Work hand in hand with AI inference engineers on the shared foundations of our self-hosted LLM stack (model registry, deployment automation, shared GPU capacity), and stay current with MLOps practices for classic ML, generative AI and agentic systems alike.

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.

Want to learn more about our tech culture and environment? Visit the Doctolib Tech site.

Our interview process

We want your experience to be clear, respectful, and transparent. Learn more about our hiring process on our candidate experience page.

We welcome everyone

At 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.

The 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.

To 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.

Join us in building the healthcare we all dream of!