Top Machine Learning Jobs in France

The role involves reinventing voice recognition technologies for real-world conversations, focusing on real-time speech processing techniques.

The Machine Learning Engineer will develop high-performance algorithms for retail decision-making, optimizing solutions and supporting data scientists in applying these algorithms effectively.

As a Research Engineer in ML, you'll optimize large-scale ML systems, collaborate with Research Scientists, enhance models, and implement algorithms in Python.

The Cloud Machine Learning Engineer will develop and integrate ML solutions with cloud platforms using Hugging Face libraries, focusing on performance and developer experience.

As a Cloud Machine Learning Evangelist, you will educate the ML community, publish technical content, and drive visibility of integrations with strategic partners.

As an Applied ML Engineer, you will build and scale data pipelines for video generation, ensuring data quality and optimizing preprocessing workflows.

The Senior AI/ML Engineer at Nansen will develop AI models for blockchain data analysis, collaborating with teams to enhance platform capabilities and mentor junior engineers.

As a Senior Machine Learning Engineer, you will design and refine deep learning models for medical applications, lead projects, and collaborate cross-functionally to integrate research into scalable product features.

The role involves implementing MLOps processes, developing scalable pipelines, and enhancing CI/CD for machine learning models while participating in software development practices.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Lead and develop the AI/ML Engineering team for Terabase's software product. Oversee AI/ML initiatives, collaboration across teams, and ensure product quality.

The Senior ML Infrastructure / DevOps Engineer will design, operate, and scale infrastructure for machine learning workloads, automating provisioning and building robust ML pipelines while collaborating with ML teams for production readiness.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

Own and drive ML solutions for clinical care at scale, including LLM fine-tuning and production deployment, collaborating across teams.

The AI/ML Engineer will develop and implement ML systems for secure-code understanding, manage infrastructure for LLMs, optimize models, and conduct research.

As a Software Engineer - ML, you'll transition research into production systems, implement CI/CD pipelines, and enhance automation workflows.

The Head of Machine Learning will manage 9 ML engineers, coordinate projects, contribute to system architecture, and promote team growth. Responsibilities include strategic leadership, project management, and technical mentorship to align ML initiatives with business goals.

Design and implement multi-modal perception models for radar sensing, ensuring real-time deployment and producing reliable outputs across conditions.

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

Design, build, and productionize LLM-based, retrieval, and multimodal AI systems. Develop RAG and agentic pipelines, deploy and monitor models in cloud-native environments, evaluate performance, and collaborate with engineering and product teams to scale robust AI solutions.

As a Senior Machine Learning Engineer, you will develop and scale customer-facing AI models, ensuring quality integration with business solutions, and mentoring peers.

The role involves conducting R&D in machine learning, particularly in model training and architecture development. Candidates should have a background in deep learning and experience in hands-on experiments with foundational models.

As a Senior Machine Learning Engineer, you will design and deploy ML models, drive technical excellence, mentor team members, and translate business problems into ML solutions.

The role involves developing and maintaining ML models for robotics, enhancing the core library, managing community interactions, and ensuring high performance benchmarks.

As an ML Ops Engineer, you'll design and maintain ML pipelines, ensure monitoring and reliability of ML systems in production, and collaborate with teams across different regions on GCP infrastructure and ML models.