Top Machine Learning Jobs in France

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

Embed within a product team to own end-to-end ML projects: frame problems, choose build vs buy, fine-tune or integrate models, ship production code, monitor performance, iterate quickly to deliver customer and business impact.

The AI Engineer will design RL environments, develop reward functions, build evaluation frameworks, and create data pipelines for AI agents in supply chain applications.

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

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.

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.

Design, train, evaluate, optimize, and deploy machine learning models and scalable pipelines for real-time neural time-series data. Build evaluation tooling, benchmarking, and production-grade ML systems in collaboration with BCI researchers and product engineers.

Develop new ML methods for neural and multimodal time-series data, design and run rigorous experiments and benchmarks, build prototypes for user studies, and collaborate with BCI scientists and engineers to deploy neuroscience-informed training and inference workflows.

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 development of a high-performance robotics simulation platform and compiler stack. Design compute infrastructure and dataflows for physics simulation and foundation-model training. Implement JIT/LLVM-based GPU codegen, optimize compile/runtime performance, add differentiable programming support, collaborate with simulation engineers, and contribute to open-source ML compiler projects.

Design, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Build, maintain, and automate reproducible training and evaluation pipelines, CI, experiment tracking, and a model registry. Integrate test automation including on-device and hardware-in-the-loop runs, ensure reproducibility and traceability, and enable modelers to focus on model quality.

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.

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.