Engineering Director - Health Companion App - Paris (x/f/m)

Doctolib

WHY THIS ROLE As our Director of Site Reliability Engineering, reporting to our VP of Platform Engineering, you'll own the core infrastructure layers that everything at Doctolib runs on: cloud infrastructure, database operations, network infrastructure, and observability. You will also lead the Doctolib Operations Center (DOC) and drive a decisive shift from reactive operations to a proactive, world-class reliability culture. This is a rare opportunity to shape the infrastructure backbone of Europe's leading healthtech company, at a moment when Doctolib is actively expanding multi-cloud capabilities, scaling to new countries, and building the reliability culture that will define the next decade of healthcare innovation. Why this is an extraordinary challenge * Real stakes, every day. When Doctolib is down, consultations don't happen, diagnoses are delayed, care journeys are interrupted. The infrastructure you build is a direct lever on patient outcomes — in a world where 8 of the top 10 causes of death in Europe are preventable. * A once-in-a-generation platform transition. Multi-cloud, monolith modularisation, international expansion — all happening simultaneously. You won't inherit a finished platform. You'll define what it becomes. * Reliability as the competitive moat. As we scale AI health companions, automate clinical workflows, and launch across Europe, the speed and resilience of the platform directly determines how fast 700+ engineers can ship innovations that change healthcare. * A cultural build, not just a technical one. The incident response culture, observability standards, and operational ownership model you establish here will shape how Doctolib engineers work for years to come. WHAT YOU'LL DO * Build and run a world-class SRE org of 25+ engineers across Cloud Infrastructure, Database & Storage, Network Infrastructure, Observability Tooling, and the Doctolib Operations Center * Own the infrastructure strategy and roadmap — cloud, database, network, observability — and deliver against company OKRs * Lead the Doctolib Operations Center: set incident response standards, drive MTTR reduction, embed blameless post-mortem culture across engineering * Architect and execute our multi-cloud strategy — reducing vendor lock-in, cutting migration costs, and enabling international expansion * Own network infrastructure at scale: load balancing, CDN/WAF, VPCs, peering, zero-trust networking across a high-traffic, multi-country platform * Drive observability as a product — give 700+ engineers true visibility into system health and turn observability maturity into an operational excellence lever * Lead from the front as a senior technical voice in the Platform org and broader Tech leadership team WHO YOU ARE * 12+ years in software engineering, including 5+ years leading managers and running infrastructure or SRE organisations at scale * Track record of taking SRE practices from reactive to proactive — with measurable reductions in incidents and MTTR * Strong multi-cloud and network infrastructure experience: load balancing, CDN/WAF, VPCs, peering, at high-traffic scale * Deep database operations background: large-scale transactional systems (PostgreSQL, Aurora), streaming/CDC (Kafka), data layer FinOps * Experience building observability platforms that give teams genuine visibility — metrics, logs, traces, alerting * Sharp process thinking: SLOs, error budgets, incident management, blameless post-mortems * Outcome-driven: you track reliability, cost efficiency, and engineering velocity as business metrics, not just technical ones * Strong communicator and influencer at executive level — equally credible with senior engineers and business stakeholders * Builder of high-performing, people-first engineering cultures * Fluent in English; comfortable in fast-paced, international environments * You recognise yourself in our playbook values Bonus Points If You Have… * Experience in healthcare, regulated, or high-compliance industries (HDS, ISO 27001, SOC2, GDPR, data sovereignty) * Familiarity with our stack: Ruby on Rails, Node.js, Go, Python, React, AWS, GCP, Kubernetes, PostgreSQL, Datadog, GitHub Actions * French language proficiency * Experience with AI-augmented infrastructure tooling or ML platform operations * M&A or post-acquisition infrastructure integration experience WHAT WE OFFER * Free comprehensive health insurance for you and your children * Parent Care Program: receive additional leave on top of the legal parental leave * 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 abroad 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 If you would like to find out more about tech life at Doctolib, feel free to read our latest Medium blog articles!   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, 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! All information provided is processed by Doctolib for application management. For data processing details, click here. Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights.   #LI-DB1

Scaleway

OUR STORY:   🇪🇺 Join Scaleway and shape the sovereign cloud of tomorrow ! Since 1999, we have been designing secure, sustainable infrastructures aimed at supporting the most ambitious companies.   Historically known for our dedicated servers (Dedibox), we made a strategic shift to cloud computing in 2015. Staying true to our principles of simplicity, flexibility, and technical excellence, we have become one of the leading players in Europe in the sector.   With the rise of artificial intelligence, we have strengthened our commitment, supported by the Iliad Group, which is investing €3 billion to develop a serious, sovereign AI alternative to American and Asian giants.   Every day, thanks to our fast-growing portfolio of cloud and AI products (bare metal, containerization, serverless, AI, etc.), Scaleway proudly serves thousands of customer across the private and public sector, from corporations like France Télévisions or Hachette Livre, to fast-growing startups like Photoroom and Biolevate, to institutions like the City of Copenhagen.   📍 Our offices are located in Paris, Lille, Toulouse, Rennes, Rouen, Bordeaux and Lyon.   WHY WE NEED YOU ?   Our growth is driving us to strengthen our Storage Tribe to lead the engineering department responsible for our foundational storage products. Your mission will be to define the global technological vision and orchestrate the engineering strategy in order to guarantee the performance, SLAs, and scalability of our distributed storage systems while managing a team of engineering managers and technical experts.   YOUR FUTURE TEAM   We work in a collaborative and international environment where the diversity of Scalers, combined with a spirit of sharing, helps bring new projects to life every day, advancing our ambitions together. You will be part of a team of 30 to 50 people. The Storage Tribe builds and operates the critical infrastructure that powers our customers' data needs at a massive scale. Managing hundreds of petabytes of data, the team develops homegrown object and block storage platforms using technologies like Ceph and distributed encoding. The team is organized into squads led by Engineering Managers and Tech Leads, covering low-level development, SRE, and production stability. Manager information: You will report directly to the Director of Engineering.   YOUR DAILY ROUTINE   - Strategic Leadership: Define and carry the global technological vision for the Storage department, challenging the technical direction of the scope. - Team Management: Manage and mentor a hierarchy of Engineering Managers, Tech Leads, and Contributors (30-50 Scalers), fostering a culture of benevolence and engineering excellence. - Operational Excellence: Guarantee the performance and reliability of our storage platforms, ensuring strict adherence to SLAs and production standards. - Architecture & Innovation: Drive decisions on low-level architecture and distributed systems, leveraging open-source technologies (Ceph, ZFS, NFS). - Cross-Functional Alignment: Collaborate with Product teams and other technical directions to align engineering output with business strategy and the sovereign cloud mission. - Crisis Management: Act as a high-level escalation point for critical technical incidents, applying pragmatic problem-solving ("rubber ducking") to resolve complex blockers. - Talent Development: Oversee recruitment and career paths, ensuring the growth of high-potential profiles in a specialized technical population.   ABOUT YOU   HARDSKILLS: - Experience: 10+ years of experience in engineering, with a strong background in low-level development, production, and storage systems. - Technical Mastery: Deep knowledge of storage technologies (Object Storage, Block Storage, Ceph, ZFS, NFS) and system programming (C, C++, Rust, or Go). - Systems Expertise: proficiency in Linux/FreeBSD internals and system profiling. - Open Source: A strong track record of contribution to or usage of open-source technologies. - Leadership: Proven experience managing managers and leading technical teams in a production environment.   SOFT SKILLS: - Benevolent Leader: You prioritize the well-being and growth of your team. - Pragmatic Problem Solver: You are an expert at "rubber ducking" and can simplify complex technical situations. - Abstract Thinking: You are capable of high-level abstraction while retaining a love for computers and deep-tech details. Open-Minded: You remain curious and adaptable in a fast-evolving cloud environment.   WHAT YOU WILL FIND AT SCALEWAY ++++ -Hybrid work: We offer up to 3 days of remote work per week. -Offices: Our offices are spacious, dynamic workspaces with bold design, conveniently located near public transport. Most of our offices feature outdoor spaces (terraces) and bike parking facilities. -Dining: Our chef provides a healthy meal service at the headquarters, and breakfast is available across all our sites year-round. Scalers working from regional sites enjoy a Swile card for lunches. -Well-being commitments: Whether it’s access to a gym, daycare places, or discounted services for caring services, Scaleway is committed to supporting Scalers in maintaining a balanced life. -International environment: With dozens of nationalities, Scaleway offers a stimulating environment where English is as widely spoken as French. -Career & Mobility: Our managers value internal mobility, and opportunities to transition to other entities within the Iliad Group are accessible to all Scalers.   🚀 Why join the Scaleway adventure? ✔ A rich and diverse product offering: Scaleway offers over 100 public cloud products in IaaS, PaaS, and AI. ✔ A cutting-edge technical environment: Scaleway provides modern infrastructures, including high-performance bare metal servers, to tackle exciting technical challenges. ✔ Commitment to responsible cloud: Scaleway is dedicated to a more responsible cloud, with data centers powered solely by renewable energy since 2017, minimizing our ecological footprint and holding top-level certification.   🔜 THE NEXT STEPS … - Discovery call with a recruiter (30 min) - Interview with the Director of Engineering to understand your technical skills and approach to the role (45 min) - Technical/Managerial interview to validate your expertise and leadership style - HR Interview to tour our offices and meet your future colleagues  

Vestiaire Collective

Vestiaire Collective is the leading global platform for desirable pre-loved fashion and a pioneer in transforming how people consume fashion.   Our mission is simple: make circular fashion the norm, not the exception. Through technology, expertise, and a highly engaged global community, we enable millions of people to buy and sell fashion in a more sustainable way.   Founded in Paris in 2009, Vestiaire Collective is now a globally scaled marketplace with offices in Paris, London, Berlin, New York, Singapore, and Ho Chi Minh City, and logistics hubs across Europe, Asia, and the US. Today, we are a team of around 600 people from over 50 nationalities, united by a shared ambition: to drive meaningful change in the fashion industry.   Our values, Activism, Transparency, Dedication, Greatness, and Collective, shape how we build, collaborate, and grow every day. About the Role We are seeking a Foundational Machine Learning Engineer for a high-impact greenfield opportunity to build our MLOps infrastructure from the ground up at Vestiaire Collective. While driving our AI authentication initiatives (deploying multi-model approaches including computer vision for luxury product authentication and counterfeit detection) will be your immediate focus, your long-term mission will be to scale foundational architecture across the entire marketplace. You will expand our ML capabilities to power broader domains, primarily focusing on search and recommendation systems, with future expansions into dynamic pricing and marketing technologies. Acting as the bridge among Applied Science, Data Platform, and Backend Engineering, you will design robust, decoupled architectures and spearhead the MLOps strategy with our Director of Data, prioritizing system maintainability, engineering hygiene, and the reliable deployment of complex models, ensuring all our ML models across the board deliver high-throughput, low-latency business impact. What You Will Do Short-Term Impact (First 6 Months): Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code. You will directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication, immediately improving our trust and safety ecosystem. Mid-Term Foundation (MLOps Lifecycle & Infrastructure): Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring. You will scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations), design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience. Long-Term Vision (Centralizing 360-Degree MLE Capabilities): Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization. You will transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms. Who You Are Must-Haves: Experience: 5-8+ years of hands-on experience in Machine Learning Engineering, specifically focused on building and scaling MLOps infrastructure and productionizing ML systems. Production Infrastructure: Proven expertise in deploying low-latency, high-throughput ML inference services (using FastAPI, TorchServe, Triton Inference Server, or Ray Serve) across both classical lightweight and heavy-width ML models (PyTorch/TensorFlow). Strong preference for AWS (EKS, EC2, SageMaker) / Snowflake and Open Source ecosystems over GCP/Azure. MLOps & Pipelines: Deep experience building automated, continuous model retraining pipelines to handle concept drift (ranging from daily to weekly cycles). You have orchestrated decoupled, multi-model AI architectures using tools like Airflow, Kubeflow, or Metaflow, and possess strong expertise in model registry and tracking tools like MLflow or Weights & Biases. Feature Stores: Hands-on experience evaluating, building, or extensively leveraging online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in a production environment. Familiarity with frameworks like Feast or custom dbt-based pipelines is highly valued. Strategic Builder Mindset: You are an analytical builder who thinks long-term. You can successfully evaluate TCO for bespoke internal systems versus enterprise tools, anticipate technical liabilities, and design robust architectures that handle unpredictable peak traffic surges. Collaboration & Engineering Hygiene: Strong cross-functional communication skills. You excel at translating complex ML prototypes into highly scalable production code backed by strict version control, rigorous testing, and CI/CD best practices, seamlessly connecting data science innovation with backend engineering execution. Nice-to-Haves: Relevant Domain Expertise: Background in E-commerce, Single-SKU Marketplaces, Search & Recommendation, Trust & Safety, or Counterfeit Detection. Vision, Edge & Optimization: Hands-on experience with Vector Databases, Visual RAG pipelines, deploying Deep Learning VLM models, and optimizing models for edge computing or low-latency inference (e.g., ONNX, TensorRT). Infrastructure & Observability: Advanced experience with containerization (Docker, Kubernetes), Infrastructure as Code (Terraform), and data transformation workflows (dbt). Familiarity with setting up advanced monitoring for model performance, concept drift, and system health (Datadog, Prometheus).