Top Machine Learning Jobs in France

Design, build, optimize, deploy, and monitor production AI/ML solutions, especially LLM and VLM applications. Develop model integrations, APIs, MLOps pipelines, RAG systems, and scalable serving infrastructure. Apply software engineering, testing, CI/CD, observability, security, and compliance practices. Own model lifecycles from data preparation and research through production maintenance. Collaborate with engineers, project managers, clients, and stakeholders while mentoring junior engineers and evaluating emerging AI techniques.

Own reliability for Vibe’s latency-sensitive prediction serving, bidding, pacing, and delivery infrastructure. Define SLOs, improve observability, reduce p99 latency and infrastructure costs, manage Redis and capacity planning, build end-to-end stress testing, and lead incident response and postmortems. Partner with data scientists and engineering teams to operate model-serving systems reliably at high scale.

Lead European and international research consortia, grants, and public-private partnerships for a TechBio company. Identify funding opportunities, build and negotiate multidisciplinary consortia, write and defend grant proposals, and manage funded projects, budgets, deliverables, and agency reporting. Serve as the link between AI and biology teams, external collaborators, and funding bodies while representing the company’s Virtual Tissue platform at global innovation and scientific events.

Develop, validate, deploy, and maintain machine learning and deep learning algorithms for EEG, cardiopulmonary, and other biosignal data used in medical devices and clinical applications. Responsibilities include data curation, model development, failure analysis, testing, documentation, regulatory-quality processes, production deployment, tooling improvements, stakeholder presentations, and client support.

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

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.

Own the Subscriptions roadmap end-to-end: research, prioritize, coordinate cross-product dependencies, drive features through delivery, and enable sales and documentation to maximize retention and revenue.

Develop and manage systematic trading strategies, optimize quantitative portfolios, and collaborate with quant traders and the investment committee.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

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.

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.

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

As an AI Product Manager, you will lead AI product development, collaborate with teams, conduct market research, and ensure alignment with user needs.

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

The Senior Machine Learning Engineer will design and implement AI and ML solutions, build retrieval pipelines, and develop language models to enhance patient care access and management.

Develop and deploy machine learning models for dynamic pricing, fleet repositioning, and marketplace optimization. Build MLOps processes, experimentation infrastructure, data pipelines, KPIs, and real-time monitoring dashboards. Collaborate with international, multidisciplinary teams to influence product strategy and business decisions. The role requires production-quality Python, SQL, BI tools, statistical expertise, and the ability to communicate insights to nontechnical stakeholders.

Conduct research advancing self-improving AI, agentic intelligence, autonomous execution, long-horizon reasoning, learning from experience, model architectures, training, and evaluation. Develop novel algorithms, research prototypes, and experimental systems, collaborating with engineers and researchers to turn frontier research into robust, scalable AI systems.

Comand AI is accepting spontaneous applications for future engineering, product, business development, and deployment roles. Potential work includes forward-deployed engineering, computer vision, applied machine learning research, full-stack and backend development, geospatial data, ML/LLM workflows, and military deployment strategy. Strong candidates operate autonomously, handle ambiguity, scope problems independently, and prioritize user outcomes. The company operates from Paris and London with a hybrid setup.

Build and ship end-to-end features for Prevail, an operational planning platform used in live defense deployments. Responsibilities include full-stack or backend development with Python, FastAPI, and TypeScript; geospatial data work using tile servers and PostGIS; resilient ML and LLM workflow orchestration with LangGraph and LangChain; and on-premise appliance deployment. The role involves working independently amid ambiguity, collaborating with leadership, engaging directly with users, and traveling to client sites when deployments require it.

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.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Design and develop bulk and single-cell multi-omics sequencing pipelines, lead omics acquisition strategy, support drug discovery projects, help design biological experiments, and contribute to academic publications and conferences. The role requires strong R, Python, and Bash programming, software engineering practices, and bioinformatics expertise, with opportunities to work on machine learning-based digital twin technology.