Machine Learning Engineer Jobs in Paris, EEA

Senior Machine Learning Engineer - Speech to Text at Nabla. Apply via Ashby.

Machine Learning Engineer (Semantic Scene Understanding) at Harmattan ai. Apply via Ashby.

Machine Learning Engineer - Foundational at Harmattan ai. Apply via Ashby.

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).

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews. Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here). Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries. We have been profitable since 2023, and we are just getting started. Our beliefs: We hire for skills and potential. With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process because the best teams are built on merit. AI at Qonto: AI is deeply embedded in how we work (here) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it. ------------------------------------------------------------------------------------------------------ Join us as a Staff Machine Learning Engineer on our AI Product team to build and ship customer-facing AI for 600,000+ business customers. You'll combine Generative AI with proven machine-learning techniques to create products with measurable impact — adoption, faster task completion, user satisfaction — while ensuring reliability, privacy, and continuous monitoring in production. ➡️ What you'll do Develop ML models end-to-end: From understanding product requirements to training, evaluating, and deploying models in production. You design, iterate, and ship — not just prototype. Integrate ML into the product ecosystem: Align with Product Managers, Data Engineers, and Backend Engineers to ensure your models are seamlessly embedded in Qonto's financial services. Build the ML Ops framework: Create the infrastructure for the team to scale — model drift detection, performance tracking, automated retraining pipelines, monitoring, and alerts. Put models into production with rigour: Robust technical implementation, quality assurance, and continuous monitoring. Client-facing AI in financial services has no room for silent failures. Raise the bar for the team: Share best practices, contribute to internal tooling improvements, and mentor peers across the ML team. ➡️ What we're looking for 6+ years as an ML Engineer with ML Ops experience: You've developed and deployed client-facing ML products end-to-end — not internal tools or dashboards. You can show measurable impact on real users. Modelling expertise: Experience building and optimising machine learning models for external customers. You know when to use GenAI and when proven ML techniques are the better choice. Strong Python engineering: You write resilient, testable code at scale. Proficient with FastAPI (or similar), third-party service integration, and database interaction in production. ML Ops fluency: Familiar with tools that automate model retraining, performance checking, and drift detection. You've built or significantly improved ML infrastructure before. Fluent in English: Qonto's working language. ➡️ What we can offer you Customer-facing AI with real impact: Your models will be used directly by hundreds of thousands of business customers. You'll see adoption metrics, not just offline evaluations. A modern, flexible stack: Python, Snowflake, Kafka, Kibana, PostgreSQL, Airflow, AWS, Prometheus, ArgoCD, GitHub, Cursor. You have the freedom to test any tool as long as it helps reach the target. A team building AI at the core of fintech: 10 AI Engineers and 3 Data Ops working on innovative solutions at the heart of Qonto's financial services — not a side project. Clear IC growth track: Individual contributor career path for those who want to become deep experts in their field, with access to the latest AI technologies. ➡️ Your future manager Option A Your manager will be Marianne Borzic Ducournau, Head of Data Products. Her background? A graduate of École Polytechnique, Marianne went on to lead Data Science teams at Uber and Amazon in San Francisco before joining Qonto four years ago to build our Data Science team from scratch — hiring the founding members and defining the technical direction. What does she bring to the team? A rare combination of applied ML expertise and business context from Finance — she helps people see both the technical and the strategic side of what they're building. Option B Your manager will be Benjamin Wolter, Head of AI Products. His background? After earning his PhD in Physics and leading ML Engineering and Data Science teams across last-mile logistics and digital marketing, Benjamin joined Qonto to lead our AI Products team. What does he bring to the team? Deep technical ML expertise, practical experience building scalable ML systems, and a management style built around ownership and autonomy — he creates the conditions for people to grow without hand-holding.

About Mistral At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life, democratizing AI through high-performance, open-source models, products, and solutions. Our comprehensive AI platform meets enterprise needs, whether on-premises or in the cloud, and includes tools like Le Chat, La Plateforme, and Mistral Compute. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed across France, the USA, the UK, Germany, and Singapore. We are creative, low-ego, and team-spirited. Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. Learn more about our culture here. About the job The Applied AI for Critical and Sovereign Institutions team is Mistral’s specialized unit dedicated to delivering high-impact, secure AI solutions for institutions and organizations operating in highly regulated and strategic environments. We work hand-in-hand with clients to design, deploy, and maintain AI systems that meet the highest standards of reliability, security, and operational excellence. Our team combines deep technical expertise with a rigorous approach to compliance and risk management, ensuring that every solution is both cutting-edge and fully aligned with the unique constraints of our partners. Mistral AI is seeking an Applied AI Engineer to join this team. You will be responsible for the technical design, implementation, and deployment of AI solutions tailored to the needs of critical infrastructure and sovereign institutions. Your work will directly contribute to projects with significant societal and operational impact. What you will do • Individually deploy AI solutions into production for use cases with significant operational and strategic impact. • Develop state-of-the-art GenAI applications tailored to the specific needs of sovereign institutions and critical infrastructure, driving technological transformation in collaboration with our customers. • Work closely with our researchers, AI engineers, and product teams on complex customer projects involving advanced fine-tuning, LLM applications, and contributions to our open-source codebases for inference and fine-tuning. • Participate in pre-sales discussions to understand the needs, challenges, and aspirations of potential clients, providing technical guidance on Mistral’s products and technologies to diverse stakeholders. • Collaborate with our product and science teams to continuously improve our offerings based on customer feedback, with a focus on security, compliance, and performance. How we work in Applied AI • We care about people and outputs. • What matters is what you ship, not the time you spend on it • Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week. • Always ask why. The best solutions come from deep understanding, not from copying what worked before • We say what we mean. Feedback is direct, timely, and given because we care. • No politics. Low ego, high standards. • We embrace an unstructured environment and find joy in it. About you • Fluent in English. • PhD or Master's in AI, Machine Learning, Computer Science, or related field. • 2+ years of experience in AI/ML • Proven track record of leading teams to deliver complex AI projects from prototyping to production. • Deep expertise in fine-tuning LLMs, advanced RAG, agentic systems, and deploying NLP applications at scale. • Proficient in Python, PyTorch, and modern AI frameworks (LangChain, HuggingFace). Cloud platforms (AWS, GCP, Azure) and MLOps tools a plus. • Strong software engineering skills: API design, backend/full-stack development, system architecture. • Excels in technical communication with technical and non-technical audiences, including executives. • Thrives in fast-paced collaborative environments and is passionate about mentoring technical talent. It would be great if you • Have experience with React or other frontend frameworks. • Have experience with Deep Learning in PyTorch • Contributed to open-source projects in the LLM or AI space. • Have experience in customer-facing roles with a focus on enterprise AI adoption. Security & Compliance criteria • Eligibility: must hold citizenship in the target territory (France for now). • Clearable: must meet all local requirements for high-level security clearance (e.g., no criminal record, fulfillment of national service obligations). Benefits 💰 Competitive cash salary and equity 🥕 Food : Daily lunch vouchers 🥎 Sport : Monthly contribution to a Gympass subscription  🚴 Transportation : Monthly contribution to a mobility pass 🧑‍⚕️ Health : Full health insurance for you and your family 🍼 Parental : Generous parental leave policy 🌎 Visa sponsorship The personal data you submit as part of your application will be processed in accordance with Mistral AI's Applicant Privacy Policy.

About Mistral    At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.   We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise needs, whether on-premises or in cloud environments. Our offerings include le Chat, the AI assistant for life and work.   We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited.   Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. See more about our culture on https://mistral.ai/careers.   Role Summary   You will be in charge of open-sourcing state-of-the-art models, whilst maintaining and improving Mistral’s publicly available libraries. Your work is critical in helping turn research breakthroughs into tangible solutions and improve Mistral's open-source ecosystem.   About the Open Source Software team   Our OSS team is embedded in our Science team and works very closely with various engineering and marketing teams. All OSS team members can fluidly move on the production / research spectrum depending on where the needs are or where their interests lie   What you will do   • Releasing our models to open-source platforms and libraries, e.g., vLLM, GitHub, Hugging Face • Maintaining Mistral’s open-source libraries (mistral-common, mistral-finetune, mistral-inference) • Create and maintain tooling and services: both internal facing (internal research) and external facing (open-source libraries) • Implement and optimize open-source and internal libraries for performance and accuracy, ensuring production readiness and employing cutting-edge technology and innovative approaches • Collaborate with the open-source community (PyTorch, vLLM, Hugging Face)   About you   • Master’s degree in Computer Science, Machine Learning, Data Science, or a related field • Experience contributing to popular open-source libraries such as PyTorch, Tensorflow, JAX, vLLM, Transformers, Llama.cpp, ... • Passion for contributing to the open-source software ecosystem • Expert programming skills in Python, PyTorch, MLOps • Adaptable, proactive, and autonomous • Attention to detail and a drive to go the last mile to build almost perfect tools • Deep understanding of machine learning approaches, especially LLMs and algorithms • Low-ego, collaborative and have a real team player mindset   Now, it would be ideal if you have:   • Experience with training and fine-tuning large language models (e.g., distillation, supervised fine-tuning, policy optimization) • Experience working with Slurm • Worked with research teams before • Experience as a core-maintainer of a popular ML open-source library   By applying, you agree to our Applicant Privacy Policy.  

About The Job   Mistral AI is seeking a Applied AI Engineer to facilitate the adoption of its products among customers and collaborate with them to address complex technical challenges.   The Applied AI team is Mistral's customer-facing technical organization. We work directly with enterprise clients from pre-sales through implementation to deploy cutting-edge AI solutions that deliver measurable business impact.   Our team combines deep ML expertise with strong customer engagement skills, operating like startup CTOs who own end-to-end project execution. By joining the team you'll will bridge the gap between cutting-edge AI research and real-world enterprise applications, ensuring our solutions are robust, scalable, and aligned with both customer needs and Mistral's technological vision.   What you will do   - You’ll individually help deploy into production use cases with a considerable business impact across various industries. - You’ll work on state-of-the-art GenAI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation. - You’ll work in collaboration with our researchers, other AI engineers, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases our open source codebases for tasks such as inference and fine-tuning. - You’ll be involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. You will provide technical guidance on our products and explain Mistral technologies to various stakeholders. - Your collaboration with our product and science team to improve continuously our product and model capabilities based on customers’ feedback   How We Work in Applied AI   - We care about people and outputs. - What matters is what you ship, not the time you spend on it - Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week. - Always ask why. The best solutions come from deep understanding, not from copying what worked before - We say what we mean. Feedback is direct, timely, and given because we care. - No politics. Low ego, high standards. - We embrace an unstructured environment and find joy in it.   About you   - You are fluent in English - You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products - You have proven experience in AI or machine learning product implementation with APIs, back-end and front-end interfaces. - You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases - You have deep understanding of concepts and algorithms underlying machine learning and LLMs - You have strong technical coding skills in Python - You hold strong communication skills with an ability to explain complex technical concepts in simple terms with technical and non-technical audiences   Ideally you have: - Contributed to open-source projects in particular in the space of LLMs - Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager - You have experience with deep learning with Pytorch

Qui sommes nous ? Chez MARGO, nos consultants travaillent sur ce qui compte vraiment : des projets complexes qui allient challenge intellectuel et impact business réel. C’est pourquoi nous accompagnons les plus grands acteurs de la finance, de l’industrie et de la tech sur leurs projets les plus stratégiques en Data Science, Machine Learning et Intelligence Artificielle. Pourquoi rejoindre la practice IA ? Vous évoluerez au sein d’une équipe encadrée par Hamza Bouanani, Practice Manager IA chez MARGO, également Lead Data Scientist chez BNP Paribas. Travailler à ses côtés, c’est intégrer une équipe d’experts passionnés, être challengé sur le plan technique et méthodologique, et contribuer à des projets à fort impact pour les clients. Vos missions Nous recherchons actuellement un Data Scientist pour un groupe industriel français  : Vous interviendrez sur plusieurs projets : - Projet pricing * Optimisation des prix à l'échelle * Estimation de l'impact des variations de prix et recommandations pour maximiser les ventes et les marges du groupe * Optimisation des prix selon les contraintes business, en interaction directe avec les métiers * Participation à la mise en production - Projet segmentation client * Amélioration de la qualité des données pour optimiser le ciblage des campagnes marketing ; * Réalisation de modèles de classification et régression simples, robustes et efficaces. - Recommandations * Définition et mise en place de réductions ciblées pour fidéliser les clients ; * Création d'outils d’aide à la décision pour les vendeurs, intégrant recommandations sur clients et produits ; * Collaboration étroite avec les métiers pour évaluer l’impact et le ROI des recommandations. - Votre rôle inclura également : * Phase de cadrage et définition des besoins avec les utilisateurs finaux ; * Phase R&D : analyse de données, tests et développement de modèles simples et efficaces ; * Validation avec les métiers et mise en production avec les équipes data engineering ; * Monitoring et suivi des modèles via dashboards Databricks. Profil recherché Nous cherchons des ingénieurs et data scientists exigeants, passionnés par l’IA et souhaitant évoluer dans des environnements complexes. - Formation bac+5 (école d’ingénieur, université, PhD apprécié) ; - Solides compétences en Python et bibliothèques ML/DL (scikit-learn, TensorFlow, PyTorch, XGBoost, etc.) ; - Expérience en projets industriels à grande échelle, idéalement avec des équipes pluridisciplinaires ; - Intérêt marqué pour les environnements exigeants où la qualité prime sur la quantité ; - Esprit analytique, sens du challenge et goût pour les projets à forte valeur ajoutée. Ce que nous offrons - Des missions ambitieuses et variées, toujours sélectionnées pour leur valeur ajoutée ; - Un accompagnement de proximité par des experts reconnus ; - Une communauté d’ingénieurs passionnés, avec workshops, conférences et échanges réguliers ; - Une culture d’exigence technique, de partage des connaissances et de développement continu. La suite ? Premier entretien : Échangez avec un recruteur et un business developer Entretien Technique : Montrez vos compétences et recevez un retour d'expérience Entretien de Motivation : Rencontrez un membre du comité de direction ENGAGEMENT POUR L'INCLUSION MARGO s'engage à offrir des opportunités égales à tous, nous cultivons un environnement de travail inclusif qui valorise la diversité.

Qui sommes nous ? Chez MARGO, nos consultants travaillent sur ce qui compte vraiment : des projets complexes qui allient challenge intellectuel et impact business réel. C’est pourquoi nous accompagnons les plus grands acteurs de la finance, de l’industrie et de la tech sur leurs projets les plus stratégiques en Data Science, Machine Learning et Intelligence Artificielle. Pourquoi rejoindre la practice IA ? Vous évoluerez au sein d’une équipe encadrée par Hamza Bouanani, Practice Manager IA chez MARGO, également Lead Data Scientist chez BNP Paribas. Travailler à ses côtés, c’est intégrer une équipe d’experts passionnés, être challengé sur le plan technique et méthodologique, et contribuer à des projets à fort impact pour les clients. Vos missions Nous recherchons un Machine Learning Engineer pour rejoindre nos équipes et intervenir au cœur de la stratégie Data de nos clients (Industrie, Finance, Énergie). Votre objectif principal sera d'industrialiser les algorithmes de Machine Learning et de garantir leur cycle de vie en production. Vous interviendrez sur trois axes techniques majeurs : 1. Industrialisation & Déploiement (Model Serving) - Transformation des modèles de recherche (Proof of Concept) en code de production robuste, testé et optimisé. - Développement d'APIs performantes (FastAPI, Flask) pour exposer les modèles aux applications métiers. - Conteneurisation des solutions (Docker) et orchestration sur des clusters (Kubernetes) pour assurer la scalabilité et la haute disponibilité. 2. Architecture MLOps & Automatisation - Conception et maintenance de pipelines CI/CD dédiés au Machine Learning (réentraînement automatique, validation des modèles). - Mise en place et gestion d'outils d'orchestration de flux de données (Airflow, Kubeflow, Dagster). - Gestion du versioning des données et des modèles (DVC, MLflow) pour assurer la reproductibilité des expériences. 3. Performance & Optimisation - Optimisation des temps de réponse (latence) et de l'utilisation des ressources de calcul (CPU/GPU). - Refactoring de code pour respecter les standards de Software Craftsmanship (Clean Code, TDD). - Gestion des Feature Stores pour centraliser et servir les variables calculées en temps réel. Votre rôle inclura également : Monitoring avancé : Mise en place de sondes pour détecter le "Data Drift" (dérive des données) ou le "Model Drift" et déclencher des alertes proactives. Collaboration transverse : Faire le pont entre les Data Scientists (mathématiques/modélisation) et les Data Engineers (infrastructure/données) pour fluidifier les mises en production. Évangélisation : Diffuser les bonnes pratiques de développement logiciel au sein des équipes Data Science. Profil recherché Nous cherchons un profil hybride, à la frontière entre le Software Engineering et la Data Science, capable de comprendre les mathématiques sous-jacentes tout en maîtrisant les contraintes de production informatique. - Formation : Bac+5 (école d’ingénieur, université) en Informatique ou Mathématiques Appliquées. - Compétences Techniques Cœurs : * Maîtrise avancée de Python et des bonnes pratiques logicielles (Git, Tests unitaires/d'intégration, Packaging). * Solide expérience sur les plateformes Cloud (AWS, Azure ou GCP) et l'IaC (Terraform est un plus). * Maîtrise de l'écosystème MLOps : MLflow, Kubeflow, Docker, Kubernetes. - Connaissances Data : Bonne compréhension des bibliothèques ML (Scikit-learn, Pandas) et Deep Learning (TensorFlow, PyTorch) pour pouvoir optimiser le code des Data Scientists. - Expérience : Vous avez une expérience significative dans le déploiement de modèles ML en production (batch ou temps réel). - Mindset : * Rigueur absolue sur la qualité du code et l'automatisation. * Goût pour la résolution de problèmes d'architecture complexes. * Capacité à travailler dans des environnements techniques hétérogènes. Ce que nous offrons - Des missions ambitieuses et variées, toujours sélectionnées pour leur valeur ajoutée ; - Un accompagnement de proximité par des experts reconnus ; - Une communauté d’ingénieurs passionnés, avec workshops, conférences et échanges réguliers ; - Une culture d’exigence technique, de partage des connaissances et de développement continu. La suite ? Premier entretien : Échangez avec un recruteur et un business developer Entretien Technique : Montrez vos compétences et recevez un retour d'expérience Entretien de Motivation : Rencontrez un membre du comité de direction ENGAGEMENT POUR L'INCLUSION MARGO s'engage à offrir des opportunités égales à tous, nous cultivons un environnement de travail inclusif qui valorise la diversité.

<p class="Lexical__paragraph"><strong><strong class="Lexical__textBold">Join Proton and build a better internet where privacy is the default</strong></strong></p> <p class="Lexical__paragraph">At Proton, we believe that privacy is a fundamental human right and the cornerstone of democracy. Since our inception in 2014, founded by a team of scientists from CERN, we have dedicated ourselves to providing free and open-source technology to millions worldwide, ensuring access to privacy, security, and freedom online.</p> <p class="Lexical__paragraph">Our journey began with Proton Mail, the largest secure email service globally, and has since expanded to include Proton VPN, Proton Calendar, Proton Drive, and Proton Pass. These tools empower individuals and organizations to take control of their personal data, break away from Big Tech’s invasive practices, and defeat censorship. Our work impacts hundreds of millions of lives, from activists on the front lines defending freedom to leaders in governments protecting sensitive information. In some cases, Proton’s services have even been instrumental in saving lives by enabling secure and private communications in high-risk situations.</p> <p class="Lexical__paragraph">Proton is a profitable company that does not rely upon VC funding, supporting over 100 million user accounts with a growing team of over 500 people from over 50 different countries, from the world's top companies and universities. We value intelligence, learning potential, and ambition in our hiring process. Adaptability is key as we navigate uncharted territories and redefine how business is conducted online.</p> <p class="Lexical__paragraph">Hiring at Proton is highly selective, with less than 1% of candidates hired. We believe smaller teams of exceptional talent will always prevail over larger teams with lower talent density. You will have the opportunity work with many of the world's top minds in their fields, ranging from former international math and science olympiad winners to chess champions.</p> <p class="Lexical__paragraph">We have a global mindset and big ambitions but remain a start-up at heart. We value empowerment and flexibility and keep our structure flat to keep moving fast and avoid unnecessary politics. Tired of blending into the crowd? Join us and do work you can truly be proud of. Check our <strong><strong class="Lexical__textBold">open-source </strong></strong>projects <a class="Lexical__link" href="https://proton.me/community/open-source"><strong><strong class="Lexical__textBold">here</strong></strong></a><strong><strong class="Lexical__textBold">!</strong></strong></p> <p><s