Top Machine Learning Jobs in France

The internship involves deploying data processing pipelines, collaborating with clients, conducting R&D in AI and data science, and supporting sales efforts.

Lead developer-facing work on advanced real-time rendering and neural graphics: optimize GPU/CPU performance, develop and tune neural rendering and AI models, collaborate with AAA studios and NVIDIA architecture/driver teams, build demos, publish and present research.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

The Developer Relations Manager will lead technical engagements across France and Southern Europe, focusing on AI and GPU adoption, establishing relationships with developers and startups, and driving industry leadership.

The role involves applying data science in AI projects, translating business needs into technical requirements, building machine learning models, and collaborating across teams to deliver solutions in a biopharmaceutical context.

The role involves delivering technical solutions, integrating AI/ML models, developing software, and mentoring junior engineers while ensuring customer satisfaction through engagement and solution design.

The role involves assisting the D-STAR Product Team with daily tasks, preparing content for meetings, monitoring user adoption, coordinating with other teams, and supporting Digital Acceleration initiatives.

The Head of Insights & Decision Science leads insight teams to provide diagnostic and prescriptive advice for B2C decision-making. Responsibilities include managing a multidisciplinary team, implementing advanced analytical techniques, and driving innovation to enhance data-driven insights.

Lead the development and deployment of AI solutions in industrial settings, bridging technology exploration and practical applications. Responsible for guiding AI decisions, collaborating with teams, and driving customer-focused solutions.

Join as a Solutions Architect intern, guiding projects from concept to delivery, focusing on data science, product expertise, and technical project management while developing AI solutions.

Conduct applied research and deploy ML models for high-throughput ad prediction systems (CTR, online learning, embeddings). Translate papers into production features, run A/B tests, monitor live model performance, and collaborate with engineering and product teams.

Lead development of ML and deep learning models to integrate multi‑omics data for target identification, pathway analysis, and patient stratification. Design representation‑learning and transformer‑based approaches, ensure model robustness and interpretability, scale experiments on HPC/cloud, and collaborate with biologists and discovery teams to translate findings into drug discovery workflows.

The Fullstack Developer will design user experiences, build reusable components, and develop products combining web technologies and machine learning while collaborating with product teams.

As a Machine Learning Engineer at Doctrine, you will develop NLP models and conversational agents to streamline legal information access and enhance product features.

The Data Scientist will assist customers in the Biopharma industry, support sales teams, implement advanced analytics, and analyze data using the Umetrics suite of solutions.

Design and improve end-to-end ML/AI pipelines, process datasets, and contribute production code for high-quality solutions in machine learning projects.

Support building and deploying data pipelines, develop and calibrate ML/NLP/image models for fraud and claims, collaborate with clients and sales, conduct R&D, and participate across the Data Science product lifecycle.

Lead the ML team by developing long-term strategy, driving innovation, and ensuring technical excellence while collaborating across departments and scaling the team.

Lead system architecture, definition, integration, and validation for robotic systems. Coordinate multidisciplinary teams, perform trade-off analyses and system-level simulations, and ensure requirements traceability through deployment.

Lead system architecture and implementation for a robotic system from concept to field deployment. Coordinate multidisciplinary teams (mechanical, electrical, software, GNC, ML), perform trade-off analyses and simulations, oversee integration, prototyping, validation, and drive design reviews, requirements traceability, and documentation improvements to ensure robust mission performance.

Design, implement, and validate automated manufacturing and test systems. Improve OEE and reliability, integrate automation with ERP/MES, build OEE dashboards and predictive maintenance, prototype robotics solutions, standardize architectures, and train technical staff.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

Own the end-to-end AI Scribe product: define strategy, improve clinical ASR quality, build ML evaluation and monitoring, collaborate with clinicians and engineers, and drive Germany market launch to reduce clinician documentation burden.

Join as a data scientist apprentice to design predictive models, analyze datasets, and contribute to data-informed solutions across the organization.

As an AI Benchmark & Datasets Engineer Intern, you'll identify and curate benchmarks, evaluate clarity and data quality, run validations, and maintain documentation for model evaluation.