Top Machine Learning Jobs in France

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

As a Senior Fullstack Engineer, you will develop a legal intelligence platform, focusing on user experience, reusable components, and ML integration while collaborating closely with product teams.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

As a PhD Investment & Strategy Associate, you'll conduct research and financial analysis focused on AI-driven technology companies, providing insights for investments while collaborating with a talented team to support strategic decision-making.

The role involves reinventing voice recognition technologies for real-world conversations, focusing on real-time speech processing techniques.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

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

Lead global adoption and optimization of the Matrix MMM tool, advise markets on A&P decisions, drive media planning/buying improvements, prioritize product features, enable local performance experts, and consolidate global media planning best practices to improve ROI.

Design and execute benchmarks, curate datasets, evaluate benchmarks, and build evaluation infrastructure for AI model assessment while collaborating with R&D.

Lead technical streams for customer Snowflake data platform implementations, provide advisory and troubleshooting, collaborate cross-functionally, document and optimize processes, and guide customers from migration to production while sharing product feedback.

Work with sales and partners to solve complex data problems using Snowflake: present demos and POCs, design solutions, build technical champions, collaborate cross-functionally, represent Snowflake at events, and enable customers through implementation and account strategy.

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.

Senior pre-sales Solutions Engineer for Splunk in Paris providing technical leadership across France: design and present solutions, lead PoCs, respond to RFIs/RFPs, collaborate with account teams, drive cross-sell/up-sell, and translate customer business needs into technical requirements to increase revenue and displace competitors.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Lead the Weaviate Database product: own roadmap and strategy, translate customer and research insights into features across schema, storage, replication, and search quality, coordinate cross-functional teams, drive developer adoption, and measure feature outcomes post-release.

Develop, test, and improve machine learning models (video classification, detection, tracking, anomaly detection, radar and RF analysis). Collect and validate training data, coordinate annotation, benchmark hardware (Jetson, FPGA, Intel CPUs), convert models across frameworks, and tune algorithms for mission-critical, edge-focused deployments.

Develop and implement 3D geometric ML and classical geometry algorithms to analyze and generate 3D intraoral scans and teeth. Build data pipelines, train/adapt 3D models, optimize test-time performance, validate robustness, and collaborate with clinicians and product teams to ship applied research into 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.

Build, train and deploy large time-series foundation models (multimodal inputs), design reproducible experiments, create data exploration tools, add datasets, deploy models to API/platform, iterate on user feedback, and mentor future team members.

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

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

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.