🇫🇷 Levallois-Perret, France · 20h ago
Senior ML Ops (x/f/m)
Doctolib
LinkedInseniorEnglish-friendly
Set a new pulse for healthcare!We are looking for a Senior MLOps Engineer to join the ML Platform team.Your mission will be to build and scale the infrastructure that brings machine learning to life at Doctolib — powering AI-driven solutions that improve the daily experience of care teams and patients across Europe. You will work in a cross-functional team developing the ML platform and tooling that underpins Doctolib's AI products, contributing directly to faster, more reliable deployment of models that have a real impact on healthcare delivery.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 doYour responsibilities include but are not limited to:Build and deploy production-grade machine learning models in close collaboration with data scientists and engineers, ensuring performance, scalability, and reliabilityDesign and maintain the MLOps pipeline, including version control, CI/CD, and monitoring of ML models in productionDevelop tools, frameworks, and best practices to streamline the model development and deployment lifecycleEnsure the availability and performance of ML systems, proactively identifying and resolving issues before they impact usersPartner with cross-functional teams to gather requirements, provide technical guidance, and contribute to the development of end-to-end ML solutionsShare and advocate MLOps knowledge across the tech community, documenting processes, standards, and best practices to drive consistency and knowledge transferWho you areBefore 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:Are proficient in Python, SQL, Shell Scripting, and Terraform, with hands-on experience building and containerizing ML pipelines with DockerHave solid knowledge of cloud platforms, particularly AWS services such as SageMaker, EC2, ECS, S3, and CloudWatch (and/or Azure equivalents)Have a good understanding of machine learning algorithms, concepts, and trends, including hands-on experience with Deep Learning frameworks — preferably PyTorchHave excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and produce clear technical documentationHave a strong team spirit, a genuine enthusiasm for learning, and a proactive sense of initiativeAre fluent in EnglishIt would be fantastic if you:Have experience with Kubernetes, GitOps tools (e.g. ArgoCD), and/or KafkaHave experience with ML model quantization, optimization, and HuggingFace technologies (Transformers, Accelerate, PEFT)Have experience with JavaScript/TypeScript and browser-based model deployment (transformers.js / langchain.js)Life at Doctolib TechOur solutions are built on a single fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to country and healthcare specialty requirements.Our stack is composed of Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native.We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision here.Want to learn more about our tech culture and environment? Visit the Doctolib Tech site.What we offerFree comprehensive health insurance (basic package) for you and your children25 days of paid vacation per year, plus up to 14 days of RTTFree mental health and coaching services through our partner Moka.careWork from abroad for up to 10 days per year thanks to our flexibility days policyLunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by DoctolibA subsidy from the work council to refund part of the membership to a sport club or a creative class50% reimbursement of your public transport subscriptionParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave and 75% during the second, covered by DoctolibEnrollment in Doctolib's long-term employee value sharing plan called DoctoGrowthFor caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological supportRelocation support in case of international mobilityAccess to the best AI tools for coding, development and dedicated trainingOur interview processRecruiter Interview (phone, 45 minutes)Hiring Manager Interview (1 hour)Case Study & Case Restitution (1 hour)Behavioral Interview / Meet the Team (1 hour or half-day immersion)At least one reference checkA copy of your criminal records (extrait de casier judiciaire B3)We want your experience to be clear, respectful, and transparent. Learn more about our hiring process on our candidate experience page.Job detailsPermanent positionTech stack: Python, PySpark, SQL, AWS SageMaker, Terraform, DockerFull-timeParis, FranceHybrid work setup (up to 2 remote days per week)Start date: as soon as possibleWe welcome everyoneAt 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!Your data privacyAll information provided is processed by Doctolib for application management. For data processing details, click here: France. Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights.Sourced from LinkedIn. Relocantly aggregates public job postings; apply on the original site.