Top Artificial Intelligence Jobs in Lyon

Welo Data seeks individuals to enhance AI by providing high-quality language data through annotation, evaluation, and prompt creation, focusing on inclusivity.

The role involves designing and implementing machine learning solutions, managing projects from idea to deployment, and optimizing models using AWS and Docker.

As a Smart Home Solution Engineer Intern, you will evaluate new smart home technologies, gather information, analyze installations, and propose new product developments.

Develop and maintain scalable backend services for generative AI applications, focusing on Python and AWS integration, while collaborating with teams.

As a Senior Full Stack Engineer, you will develop AI-powered tools and enhance Postman's API Network, focusing on both frontend and backend improvements.

The AI Backend Python Engineer will design and develop backend systems for generative AI applications, overseeing API delivery and AWS infrastructure while mentoring junior engineers.

The AI Backend Python Engineer will lead the architecture for AI backend systems, focusing on integration, design, and mentoring engineers while ensuring performance and scalability across platforms.

The role involves developing features for GitLab's AI-powered platform, advocating for quality improvements, and collaborating within a remote team.

Lead the Docugami Europe AI research lab, manage a team of researchers, propose solutions to scientific problems, and collaborate with academic institutions.

As a Data Science Graduate Student Intern, you will research solutions to scientific problems, collaborate with academia, and deploy findings in production with engineers.

As the Data/AI Product Manager, you will lead the product roadmap for the data squad, focusing on AI initiatives, data pipelines, user discovery, and delivering scalable solutions, while communicating effectively with stakeholders.

As a Software Engineer, AI, you'll train large-language models by comparing, ranking, and refining AI-coded snippets, while providing feedback for reinforcement learning.

Train large-language models (LLMs) by evaluating, ranking, repairing, and refactoring code. Provide detailed feedback and maintain the RLHF pipeline.

The Software Engineer will analyze, rank, and improve AI-generated code, providing feedback for reinforcement learning in a remote role.

The role involves training large-language models to improve code generation, reviewing and editing AI-generated code, and providing feedback for reinforcement learning refinement.

As a Staff Backend Engineer, you'll build Python web services, lead technical projects, and mentor engineers while driving AI integration and system performance optimization.

As a Data Engineer, you will design data pipelines, collaborate with teams to develop data models, implement quality checks, and maintain automated processes in a fast-paced environment, focusing on AI and digital innovation in manufacturing.

The Head of AI will lead applied-AI projects, design large-scale systems, prototype workflows, and manage a team of AI engineers, ensuring integration with cybersecurity processes.

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

The role involves engineering, testing, and the development of advanced frontend components for an AI-driven cybersecurity product, integrating AI features and ensuring best practices are followed.

The Fullstack Engineer will focus on product software engineering, agents framework engineering, and R&D in AI and cybersecurity. Responsibilities include hands-on engineering, testing, and integrating AI Agents into existing systems.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

As a Senior AI Deployment Engineer, you'll develop and deploy AI software on cloud and edge devices, optimize AI models, and work with microservices and MLOps.

Enable internal teams to design, build, and scale AI-powered automations using n8n, focusing on requirements discovery, workflow implementation, and lifecycle ownership.

Develop and optimize low-level kernels for AI inference, improve inference engines, debug performance issues, and collaborate across teams.