Top Machine Learning Jobs in Paris

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.

Build Solidgate’s new ML/AI function, developing production machine learning models, recommendation systems, and real-time transaction-routing decision systems. Own experimentation from ideation through A/B testing and deployment, engineer features from high-cardinality transactional data, integrate emerging AI technologies, and collaborate with Product, Engineering, and Data Platform teams on training and analytics pipelines.

Build and grow Cloudera’s sovereign cloud business across EMEA. Develop provider and customer relationships, create marketplace offerings, lead discovery and value discussions, coordinate cross-functional teams, enable partners, generate pipeline, and drive sales motions. Own regional sovereign cloud pipeline, forecasts, bookings, and revenue. The role requires enterprise cloud, data, AI/ML sales experience, sovereign cloud expertise, complex deal leadership, executive communication, and strong hunting skills.

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.

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.

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.

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 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.

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.

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.

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.

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.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

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.

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.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.