Top Machine Learning Jobs in France

Partner with universities, research labs, and faculty to drive adoption of NVIDIA accelerated computing and AI technologies. Integrate NVIDIA frameworks, libraries, GPUs, CPUs, networking, and software into scientific workloads at supercomputing scale. Lead research collaborations, trainings, workshops, lectures, and demonstrations; mentor researchers; track emerging trends; develop prototypes; and provide feedback to NVIDIA Engineering. Support publications, conference presentations, and technical content while serving as a trusted technical advisor.

Develop Python tools for quantitative trading research, improve simulation and backtesting frameworks, collaborate with quantitative researchers to enhance trading strategies, and support EMEA-focused research. The intern will work with distributed global teams and gain exposure to large-scale systems, workflow orchestration, and production-grade software engineering practices.

Supports responsible enterprise AI adoption by monitoring market trends, evaluating AI solutions, assisting onboarding and rollout, and developing training, coaching, guidance, and communications. Provides user support on prompting and agentic AI, identifies adoption barriers, translates technical developments into business language, and promotes compliant, productive AI use across workforce teams.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Lead enzyme screening and biocatalytic process development for small-molecule APIs. Apply reaction engineering, kinetic modeling, DoE, machine learning, and robotics to optimize and scale processes, transfer to pilot scale, document results, and drive digital transformation across international multidisciplinary teams.

Lead Trustpair’s global Fraud Operations function and approximately 30 employees across internal and BPO teams. Own operational strategy, metrics, quality controls, process improvement, automation, AI use cases, and capacity planning. Develop vendor-payment-fraud expertise and translate frontline insights into product and detection-model improvements. Manage external partnerships and collaborate with Product, Engineering, Customer Success, Care, and Sales, while supporting high-stakes client situations.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

The Data Architect will evolve Rexel’s enterprise data architecture and design its cloud-native data platform. Responsibilities include data modeling, integration, governance, quality, metadata, observability, data cataloging, lineage, batch and real-time pipelines, and AI-ready data structures. The role establishes standards, leads architecture governance, evaluates emerging technologies, supports analytics and machine learning initiatives, and collaborates with country IT teams, data engineers, analytics teams, and business stakeholders. Significant offshore-team experience and hands-on Snowflake expertise are required.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Lead end-to-end architecture, implementation, and delivery of Blue Yonder Warehouse Management and related execution solutions. Serve as the primary client contact, manage timelines, budgets, risks, and deliverables, and lead solution teams and integrations. Provide functional and technical guidance, mentor junior staff, align solutions with business objectives, and collaborate with project managers, delivery partners, and stakeholders to deliver successful client outcomes.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Own the end-to-end Juno AI shopping assistant experience, including conversational UX, recommendation logic, user testing, roadmap, rollout strategy, quality metrics, and competitive positioning. Collaborate with design, engineering, and the Catalog team to define and launch a new AI-powered product from the ground up.

Design and operate production backend services and AI infrastructure, including model-serving pipelines, inference services, data pipelines, RAG systems, vector stores, APIs, and microservices. Build scalable real-time and batch systems, integrate LLM, computer vision, and machine learning models, and manage deployment, observability, reliability, and performance. Collaborate with ML, product, and field engineering teams, document architectures, and mentor engineers.

Develop and demonstrate NVIDIA GenAI solutions on hyperscaler infrastructure, primarily GCP. Advise customers and partners, optimize GPU-accelerated training and inference pipelines, conduct proof-of-concept evaluations, and collaborate with Engineering, Product, Sales, and Marketing teams to integrate NVIDIA technologies into enterprise architectures.

Supports Pernod Ricard’s global Commercial Excellence team through market assessments, performance analysis, executive reporting, commercial standards, best-practice benchmarking, training materials, dashboards, and internal communications. Contributes to deployment of the D STAR machine-learning recommendation tool by monitoring roadmaps and KPIs, coordinating steering committees, prioritizing enhancements, and translating market needs into technology requirements. The role also supports strategic commercial frameworks and roadmap evolution.

Leads complex enterprise engagements by defining end-to-end architecture for large-scale, multiyear AI, data, CCaaS, and omnichannel transformation programs. Advises executive customers, resolves complex integration and architecture issues, establishes global reference architectures, influences product roadmaps, mentors solutions architects, and represents the company as a thought leader in AI-powered customer experience and enterprise architecture.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Six-month Operations internship supporting analysis, diagnostics, hypothesis-driven recommendations, and deliverable preparation across Engineering, Procurement, Manufacturing, Supply Chain and Digital Operations. Perform data analysis, develop recommendations, manage simple workstreams, interact with clients internationally, and build domain expertise. Potential conversion to a permanent role.

The role involves delivering technical solutions, integrating AI/ML models, developing software, and mentoring junior engineers while ensuring customer satisfaction through engagement and solution design.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

As a Machine Learning Engineer at Doctrine, you will develop NLP models and conversational agents to streamline legal information access and enhance product features.

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.

Lead the development and delivery of electronic warfare and SIGINT systems from concept through field deployment. Responsibilities include managing a multidisciplinary EW engineering team, defining system architectures, coordinating mechanical, electrical, software, GNC, and ML teams, performing trade-off analyses and simulations, overseeing integration and validation, and maintaining requirements traceability, documentation, and testing consistency.