Top Machine Learning Jobs in France

The Product Manager will integrate AI and automation into operations, driving product strategy, collaboration, and market advancements in trading and risk management.

The Senior Quality Operations Engineer will manage quality systems, ensure compliance with regulations, oversee audits, and optimize workflows in medical technology.

Lead a team developing AI solutions focusing on computer vision and generative AI, integrating ML into production workflows, and ensuring ethical practices.

As a Machine Learning Engineer, you'll design and deploy ML models, optimize performance, and maintain code quality. You'll also work on GenAI solutions for user inquiries and improve ML practices with team collaboration.

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

Intern will analyze existing flight data systems, understand big data systems, propose optimization solutions, and implement those in the current infrastructure.

As an Applied AI Intern, you will support the deployment of AI applications, collaborate with teams on customer projects, and enhance product capabilities based on user feedback.

Design and improve end-to-end ML/AI pipelines while building high-quality production code and addressing new use cases. Collaborate with client data and enhance LLM training.

You will conduct model training, improve architectures, design experiments, and oversee team activities. A strong background in machine learning research is required.

Lead the AI/ML team to develop innovative solutions for environmental challenges, emphasizing strategic guidance, collaboration, and team empowerment.

Design and maintain ML code for LLM safety, implement techniques for robust evaluation, and collaborate with product and engineering teams.

The role involves owning an LLM safety domain, generating synthetic data, training LLMs, conducting benchmarking, and developing techniques for safer models. Co-author research papers with the team.

The intern will assist the applied machine learning engineering team in developing recommender systems, training models, and enhancing the core recommendation platform.

As a Machine Learning Engineer, you'll optimize monetization using ML models, drive initiatives from ideation to deployment, and collaborate on model development.

As a Software Engineer on the Applied ML team, you will design and implement solutions for customers, utilizing AI and ML technologies throughout the product lifecycle.

As a Software Engineer in Applied ML, you'll design solutions for enterprise AI, focusing on product lifecycle and large language models.

The Senior ML Engineer will enhance fine-tuning methodologies, research inference optimization techniques, and re-implement LLM architectures focusing on distributed training and efficiency.

The Applied AI Engineer will onboard clients on Mistral AI products, manage complex integrations, and drive technological transformations through collaboration with stakeholders.

Lead the development of machine learning infrastructure for cybersecurity, driving production ML systems from experimentation to deployment, while mentoring junior engineers.

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

Research and implement physics-informed AI algorithms and workflows, supervise teams, publish research, and collaborate for Autodesk's design and engineering tools.

Responsible for developing AI/ML solutions in cloud-based environments, optimizing pipelines, integrating ML models, and mentoring junior staff.

The Senior MLOps Engineer will develop, deploy, and maintain machine learning models, ensuring performance and scalability, while collaborating with data scientists and engineers.

Design machine learning algorithms and build scalable model-serving infrastructure. Collaborate effectively with core teams on ML solutions and lead discussions.

Lead the architecture of backend services for real-time model inference, optimizing ML models and bidding strategies while overseeing performance and scalability across the infrastructure.