Top Machine Learning Jobs in France

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.

As an Open-Source Machine Learning Engineer, improve and maintain open-source ML libraries while collaborating with users and contributors across the community.

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, 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.

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.

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.