Top Machine Learning Jobs in France

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

Lead and manage two Voice teams (8 engineers) building ASR and voice AI products. Own technical direction, architecture decisions, roadmaps, hiring, performance management, and cross-team coherence for production ML systems.

Lead architectural strategy for large, multiyear enterprise CX and CCaaS transformations. Act as senior technical advisor, set AI/data/omnichannel standards, resolve complex integrations, influence product roadmap, establish reference architectures, and mentor senior SA teams.

Support design, maintenance, and operation of SCOR's AI governance framework, ensuring regulatory readiness (EU AI Act), risk management, standards, playbooks, and oversight of internal and client AI solutions. Collaborate with legal, compliance, risk, data, and technology teams to embed ethical, secure, and pragmatic AI guardrails, assess AI risks (bias, explainability, robustness), and promote governance-by-design and enablement across the organization.

Design and develop ML/DL models to integrate multi‑omics data for target identification, pathway analysis, and patient stratification. Apply transformers, representation and self‑supervised learning in HPC/cloud environments. Ensure model robustness, interpretability and reproducibility while collaborating with biologists and drug discovery teams to translate biological questions into AI solutions.

Own Hugging Face’s open-source speech-to-speech library and build hf-voice into a production voice-agent platform. Responsibilities include architecture, realtime inference, ASR/TTS/model integration, streaming APIs, WebSockets/WebRTC, GPU serving, concurrency, autoscaling, observability, reliability, load testing, documentation, community contributions, and deployment support. The role requires substantial Python open-source experience, realtime systems expertise, production LLM or multimodal model experience, and interest in conversational AI.

Lead end-to-end execution of complex customer deployments for physical AI and robotics systems. Coordinate cross-functional engineering, hardware, AI, infrastructure, and customer teams; manage milestones, site readiness, testing, validation, logistics, and launch. Engage enterprise customers, surface risks, resolve technical blockers, and build scalable deployment and onboarding processes.

Join a product squad to build NLP and ML solutions for legal documents: develop, deploy, monitor models (including LLM-based systems), design conversational agents, collaborate with engineers and product teams, and contribute to MLOps and engineering best practices.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Support the international sales team by optimizing AI-driven lead generation, CRM workflows, and sales pipeline efficiency. Research and qualify prospects, run multichannel outreach, analyze sales KPIs, propose automation (Python, Zapier, Make, ChatGPT agents), contribute to market intelligence, assist in opportunity qualification and closing, and collaborate with technical teams to align product capabilities with client needs.

The R&D Data Science Group Leader manages teams in data science and prototyping, overseeing projects, strategy, and collaboration to drive innovation and deliver actionable insights.

Six-month full-time internship to research and implement state-of-the-art computer vision and deep learning models, integrate CV with LLM applications, and collaborate with the ML engineering team to deploy models used by solar and roofing customers.

Six-month Operations internship in Paris supporting consulting engagements across sourcing, supply chain, manufacturing, and digital operations. Conduct analyses, diagnose performance issues, develop recommendations, prepare deliverables, manage simple workstreams, interact with clients, and develop expertise in operations domains. Opportunity for travel and potential full-time offer if successful.

Conduct PhD research developing deep-learning methods to generate reference pictures for improved interprediction in video coding. Implement algorithms in reference software, evaluate coding efficiency and complexity, contribute to standards (MPEG/JVET), publish results, and collaborate between INRIA and InterDigital labs.

Provide SAP S/4HANA functional consulting to major clients across sectors: support implementations and migrations across project phases, deliver business analysis and technical project support, and apply deep expertise in SAP modules (FI, CO, MM, SD, PP, PM, EWM), Fiori, SAC, BTP and migration tools. Work in multidisciplinary teams using Agile/Scrum/Waterfall, communicate in English, and proactively solve client problems.

The role involves performing data-related tasks supporting AI content improvement in French, including data collection, evaluation, annotation, and object tagging.

Provide end-to-end GDPR and privacy compliance, own data subject requests and compliance documentation, embed privacy into product and ML workflows, lead commercial contracting for sales, support other legal needs (trademarks, corporate, labor), and potentially grow into the DPO role.

Design, develop, and deploy end-to-end ML/AI solutions: data extraction, modeling, production deployment on container/cloud infrastructure, and monitoring. Work on scoring, document/image extraction, embeddings, knowledge graphs, and financial modeling in small collaborative teams while following software engineering and CI/CD best practices.

Lead product vision and delivery for complex technical platforms (ML, data, middleware). Translate user and executive needs into actionable roadmaps, collaborate with engineering, incorporate customer feedback, and apply hands-on technical understanding to drive data-driven product decisions.

Design, develop, test, deploy, and monitor high-scale, low-latency video SSP systems. Collaborate with engineers, data scientists, product and management to run experiments, ship features, and ensure performance, scalability, reliability, and observability in production.

Build and deploy production-ready Generative AI and LLM solutions across Aramis Group, including conversational agents, voice applications, RAG architectures, and integrations with APIs and vector databases. Develop AI pipelines using Python, SQL, Docker, and cloud technologies; monitor and optimize production systems; investigate incidents; evaluate technical solutions; and share findings with an international, multidisciplinary team.

Design, develop, and evaluate machine learning approaches for prediction, optimization, recommendation, and personalization. Convert research concepts into robust production systems processing petabytes of data and serving real-time decisions at massive scale. Build and evaluate deep learning models, conduct offline experiments and online A/B tests, collaborate across research and engineering teams, and contribute to AI strategy and technical excellence.

As a Machine Learning Engineer, you will design and optimize models for tactical robots, leveraging self-supervised learning from EO and IR data.

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.