Top Machine Learning Jobs in France

Build and operate ML platform systems for training, evaluation, deployment, and serving of generative models. Improve reliability, scalability, performance, observability, and automation for GPU and cloud workloads. Develop internal tooling and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and operational improvements.

Own and scale Vibe's identity stack: design pipelines, improve clustering to resolve users/households from event data, operate production ML services at US scale, manage cost/performance tradeoffs, run end-to-end projects, and partner cross-functionally to deliver identity-resolution tooling.

Develop and deploy AI/ML models (anomaly detection, classification, regression, NLP/LLMs) using ESA's structured and unstructured data. Work end-to-end across data engineering, model development, testing, MLOps, and software integration. Communicate results to senior management and present at conferences.

The Principal Machine Learning Engineer will lead cross-functional AI initiatives, define ML architecture, establish evaluation frameworks, and influence technical standards across teams.

As an Open-Source Machine Learning Engineer, improve and maintain open-source ML libraries while collaborating with users and contributors across the community.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Lead applied ML efforts to embed LLMs and agentic workflows into production, focusing on anti-fraud, automated evaluation, and operational automation. Design reusable architectures, implement observability and A/B tests, partner with delivery and clients, and set engineering standards for prompts, agents, pipelines, and CI/CD.

Design, prototype, and deploy ML models for personalization in mobile games. Implement and evaluate algorithms (including RL and generative methods), run A/B tests, convert prototypes to production via MLOps, monitor player data, and collaborate with product, engineering, and analytics teams to improve retention and monetization.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

Lead architecture, deployment, and scaling of ML workflows to optimize quantum chip performance. Collaborate with physicists to translate constraints into algorithms, own technical roadmaps, mentor engineers, and define engineering standards for production-grade ML systems.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

The Senior AI & Machine Learning Engineer will design and develop AI systems, implement ML algorithms, and collaborate on applications in AI and Finance.

Lead ML infrastructure upgrades in hyperscale data centres across EMEA: manage contractors, schedules, logistics, mechanical/electrical/fibre coordination, commissioning (L3/L4 support), risk mitigation, reporting, and lessons learned throughout full project lifecycle.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.

The role involves designing, implementing, and optimizing backend services for ML models in real-time bidding systems, ensuring performance and scalability.

Join a cross-functional AI team to implement and operationalize ML solutions: build scalable MLOps pipelines, deploy production-quality services with CI/CD, testing and containerization, apply ML libraries and GenAI (LLM/agents), and improve go-to-production processes.

Design, develop, and deploy end-to-end ML and LLM-powered features (RAG, prompting, OCR). Build ML/LLMOps pipelines, model versioning, monitoring, APIs/microservices, collaborate with Data/Product/Engineering, and productionize scalable, reproducible ML solutions.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Lead the development of computer vision models for healthcare robots, optimizing them for edge deployment and managing MLOps pipelines.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.