Top Machine Learning Jobs in Paris

Lead the development of computer vision models for healthcare robots, optimizing them for edge deployment and managing MLOps pipelines.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

Design, implement, and scale evaluation frameworks and automated pipelines to monitor safety, reliability, and alignment of agentic LLM systems. Define metrics, run experiments assessing reasoning, factuality and robustness, collaborate with model developers and clinicians, and drive iterative improvement and internal knowledge sharing.

Train, fine-tune, and deploy large-scale multimodal models (vision, video, audio, speech). Build multimodal data pipelines, run experiments, optimize MoE and inference (quantization, distillation, low-latency), and develop multimodal alignment/RLHF pipelines. Define evaluation benchmarks and ship models to production.

Train and post-train LLMs using SFT/RLHF/DPO, build reward models and large-scale data pipelines, run distributed multi-GPU training, design evaluation benchmarks, and optimize models for production inference and serving.

Build and maintain scalable ML infrastructure for LLM post-training, RL, evaluation, inference, and agentic workflows. Design data-control systems, optimize training and inference performance across networking, storage, and compute, and deliver tooling for experiments, evals, dashboards, reproducibility, and failure inspection. Collaborate with researchers and product to iterate models and infrastructure.

Distill and compress large vision foundation models into efficient detectors and re-identification modules for constrained edge and embedded hardware. Optimize models (quantization, pruning, LoRA), build and maintain MLOps training/evaluation pipelines, curate datasets, benchmark latency/performance, and collaborate with systems and mission teams to deploy robust solutions.

The role involves conducting R&D in machine learning, particularly in model training and architecture development. Candidates should have a background in deep learning and experience in hands-on experiments with foundational models.

Lead ML infrastructure upgrades in hyperscale data centres across EMEA: manage contractors, schedules, logistics, mechanical/electrical/fibre coordination, commissioning (L3/L4 support), risk mitigation, reporting, and lessons learned throughout full project lifecycle.

As an AI/ML Engineer, develop and optimize models for mobile apps, collaborate with teams, and implement AI techniques to enhance user experience.

The role involves designing machine learning algorithms for real-time semantic scene understanding from UAV imagery, optimizing for performance and deployment in tactical environments.

Build foundational MLOps infrastructure and deploy multi-model CV and VLM systems for authentication and fraud detection. Design data/feature management, model tracking, serving, monitoring, and automated retraining pipelines. Scale ML capabilities for search, recommendations, pricing, and marketing while ensuring low-latency, high-throughput production services and strong engineering hygiene.

You will optimize ML infrastructure, automate ML pipelines, manage versioning, and collaborate with engineers to ensure model scalability and reliability.

Design and develop machine learning models for speech recognition at Sonos, focusing on audio-based components and collaborating with teams for performance optimization and mentoring.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

As a Senior Machine Learning Engineer at Sonos, you will develop and optimize AI models for voice control systems, ensuring efficient performance across various hardware.

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.

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

Lead design and operation of a production-scale data platform for robotics: capture, storage, versioning, high-throughput training pipelines, dataset production from RL/continuous loops, tooling for visualization and debugging, and growth of compute/training infrastructure to keep GPUs fed and enable a continuous data-to-model loop.

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

Conduct R&D on web page understanding to build ML-based security features (autofill, phishing detection). Collect and process large datasets, train and deploy models, maintain pipelines, integrate LLM/RAG systems, and collaborate with engineering and product teams to productionize privacy-preserving AI features.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.