Senior AI & ML engineer

Winamax

Basés en plein cœur de Paris, nous faisons bouger l’industrie des jeux en ligne. Leader du poker et des paris sportifs en France avec 350 000 joueurs et 600 000 parieurs mensuels, nous sommes présents en Espagne, en Allemagne et bientôt en Italie et au Portugal. Nous offrons à nos joueurs une expérience exceptionnelle, à la fois technique, créative et qualitative. Innovation, unité et appartenance : nos valeurs façonnent une culture d’entreprise multiculturelle et unique.   Et si tu avais un futur dans le jeu ?   Techniquement, Winamax c’est : Un moteur de jeu puissant et innovant : adaptation dynamique de l'offre de jeu, parties de poker hautement configurables, détection de fraude Une architecture distribuée et scalable conçue pour traiter en temps réel les flux de la plateforme Une plateforme modulaire soumise à des contraintes de haute disponibilité et de sécurisation des données, capable de gérer de fortes audiences A PROPOS DU POSTE   Pour accompagner le développement de Winamax sur de nombreuses problématiques liées à l’analyse de données, nous recrutons un ML /AI Engineer.   Au sein de la team Data, tu participeras activement à la recherche et au développement de modèles de machine learning et d’IA générative afin de répondre à des problématiques complexes en optimisant les processus décisionnels, en automatisant des tâches, et en concevant des solutions innovantes adaptées aux besoins des utilisateurs.   Tu seras également impliqué(e) dans l'évaluation des performances des modèles, leur déploiement en production, ainsi que la veille technologique pour rester à la pointe des avancées en intelligence artificielle.   Tu as l'occasion unique de contribuer au développement de l’équipe avec un terrain de jeu fertile en attente d’innovation et de créativité !   LES MISSIONS DU POSTE   Identifier les besoins et les problématiques métiers et proposer des solutions concrètes et pragmatiques Concevoir et maintenir des modèles de machine learning / IA générative Collaborer étroitement avec les équipes techniques et produit Effectuer une veille active sur l’ensemble des sujets ML / IA   QUELQUES EXEMPLES DE PROJETS   Conception d’un Chatbot POC d’IA générative (génération de documentation technique, veille réglementaire, RAG à usage interne ...) Analyse et modélisation de données sur différents domaines (intégrité de jeu, addiction, …)   ENVIRONNEMENT TECHNIQUE   Stockage : RDS, S3, DynamoDB, Redis, Redshift Orchestration de containers : ECS Fargate / EC2  Jobs : Lambda, Batch Workflows : AirflowML : SageMaker / ML Flow BI : Quicksight Langages : Python, Javascript (Node) Streaming : Kafka CI : Github Action Monitoring : Cloudwatch, Grafana

Doctolib

JOIN OUR MISSION, JOIN DOCTOLIB!  We are looking for a Senior AI Engineer to join the Patient team in Paris.  The Patient domain sits at the heart of Doctolib's mission: ensuring everyone has better access to the care they need, receives better care from health professionals, and can actively prevent health problems to improve their wellbeing. You’ll design the search and recommendation engines behind our health companion, helping 100M patients across Europe instantly navigate to the exact care they need while delivering trusted, curated insights at every step. The retrieval and recommendation architecture you own will directly shape how relevant, fast, and trustworthy that experience is for every one of them. Your responsibilities include but are not limited to: * Design and build the production search & recommendation architecture: full retrieval, ranking, reranking pipeline with standard and off-the-shelf components (vector search, semantic retrieval, LLM/managed rerankers). * Establish strong baselines first (prompts, RAG, model selection) before reaching for custom ML. * Build evaluation and observability into every stage, with offline and online evaluation. * Set up the data/event feedback loops that drive iteration and feed deeper ML later. * Improve search relevance and ranking on Patient facing products , raising result quality * Own production quality: latency reliability, monitoring, and maintainability. * Partner with ML Engineers and collaborate closely with PMs and SWEs to define, build, and ship AI-powered features that deliver measurable value to users and the business.  WHO YOU ARE 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. You could be our next team mate if you have: 1. Production deployment: ability to ship algorithms to production (ECS-based service on AWS) 2. Strong analytical mindset: result-oriented,  patient-first approach 3. Significant experience as a Software or/and AI engineer shipping search or recommendation systems to production.  4. Hands-on experience building end-to-end retrieval: ranking,  reranking pipelines and familiar with nDCG, MAP, Recall@k, MRR 5. AI-engineering proficiency: turning foundation models and off-the-shelf components into production systems: embeddings & vector search, semantic retrieval, RAG, LLM-based or managed rerankers (e.g. Vertex AI). You can succeed without training a model from scratch 6. Architecture-first approach: you build the system, baselines, evals, and feedback loops with standard tooling before reaching for custom ML, and know when to partner with ML Engineers to break a ceiling 7. Evaluation & observability built into every stage (retrieval, ranker, reranker) — offline and online eval, A/B testing, position-bias handling, monitoring 8. Production deployment — ability to ship reliable, low-latency services to production (hundreds-of-ms SLAs), with care for data quality and long-term maintainability   Now, it would be fantastic if you: * Experience at a B2C marketplace (e-commerce, hospitality, travel) * Additional ML methodologies: pattern mining, recommendation systems, experimentation, or causal inference * Have experience with search engines or information retrieval concepts * Have exposure to learning-to-rank or feature engineering (for breaking ceilings later, alongside ML Engineers) * Have experience in a healthcare or other regulated domain (GDPR / HDS) WHAT WE OFFER * Free comprehensive health insurance (basic package) for you and your children * 25 days of paid vacation per year, plus up to 14 days of RTT * Free mental health and coaching services through our partner Moka.care * Work from abroad for up to 10 days per year thanks to our flexibility days policy * Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib * A subsidy from the work council to refund part of the membership to a sport club or a creative class * 50% reimbursement of your public transport subscription * Enrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth * ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave, and 75% during the second, covered by Doctolib * For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support   THE INTERVIEW PROCESS  * A first interview with a member of the Talent Acquisition team * A coding interview * An agentics system design  * A final interview  * Reference check    JOB DETAILS * Permanent position * Full Time  * Workplace: Levallois * Start date : As soon as possible     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: Germany l France l Italy l Netherlands. Please contact hr.dataprivacy(at)doctolib.com to exercise your rights.

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).