Top Machine Learning Jobs in France

Lead and design scalable backend systems, set technical vision, drive large cross-team projects, raise engineering standards, mentor engineers, and influence company-wide architecture and processes to support product growth and AI initiatives.

Design and develop UWB, radar and camera-based data fusion algorithms and sensing features. Perform DSP analysis, feature extraction, algorithm optimization, AI model integration, end-to-end integration and validation, and cross-team collaboration for automotive sensing products.

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.

As a Growth Manager, you'll optimize user acquisition across multiple apps using innovative strategies while collaborating with various teams to boost efficiency and adapt to industry trends.

You will analyze creative performance, automate workflows, scale production, partner with User Acquisition teams, and improve creative processes.

Lead and expand a cornerstone financial-institution account through strategic relationship management, tailored enterprise solution design, complex deal structuring, client advocacy, and executing business plans to drive revenue growth and long-term profitability.

As a Senior Data Scientist at Back Market, you will design and implement machine learning models to optimize pricing strategies, collaborate with engineers, and communicate findings to stakeholders.

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.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

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

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.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

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.

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.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

The role involves developing NLP models, maintaining them, designing conversational agents for legal drafting, and collaborating with various teams to innovate legal AI solutions.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

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

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

Own and optimize model compilation and delivery for edge accelerators; build compile/delivery pipelines, benchmarking and profiling tools; contribute to inference execution providers and SDK; coordinate with on-board software and runtime teams to meet power, thermal, and performance budgets.

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.

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.