Top Artificial Intelligence Jobs in Paris

Source and engage elite research scientists, software engineers, and robotics/infra talent using targeted channels (papers, GitHub, conferences). Build high-converting outreach, map markets, maintain pipelines, and operationalize scalable sourcing (Ashby, automation, enrichment). Measure response rates and pipeline health to iterate.

Full-cycle technical recruiter focused on sourcing and closing elite Research Scientists, Software Engineers, and Robotics engineers. Partner with founders and technical leaders to design searches, build scalable sourcing and screening workflows (in Ashby), run a high-quality candidate experience, and automate recruiting processes to scale hiring.

Full-cycle recruiter responsible for sourcing and closing elite Research Scientists, Software Engineers, and Robotics engineers. Partner closely with founders and technical leaders, build scalable sourcing and screening workflows (Ashby/ATS), run a fast, candidate-friendly hiring process, and use data and automation to optimize pipeline health and conversions.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines, kernels, and networking. Implement low-level CUDA/cuDNN/Triton kernels, tune hardware-software interactions, and develop monitoring and debugging tools for large-scale training runs to maximize utilization and reduce time-to-convergence.

The role involves designing and evolving Ipsen's Data & AI platforms architecture, ensuring optimal use of the platform, and collaborating with project teams. Key responsibilities include defining architectures, promoting best practices, and contributing to the platform roadmap while aligning with stakeholders.

Write and shape Sidetrade's equity story for institutional investors: prepare earnings presentations, press releases, investor decks, analyst Q&A, roadshow materials, and investor communications in English and French. Manage investor CRM, organize meetings and shareholder events, run market and competitive research, and support CEO/CFO preparation for financial communications while using AI tools like Claude.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Design, build, and run production agentic workflows and reusable components for delivery processes. Implement RAG-backed knowledge bases, monitoring, evals, observability, and human-in-the-loop feedback to improve agents and integrate them with delivery teams and business tools.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

Senior Director analyst providing thought leadership and actionable research on infrastructure and operations AI strategy. Produce insights, presentations, and client advisories; guide AI infrastructure, cloud adoption, integration, governance, and vendor selection. Lead research agenda, mentor analysts, collaborate cross-functionally (infrastructure, engineering, enterprise architecture, cybersecurity), support sales, and represent Gartner at events. Drive process improvements and influence senior stakeholders to align infrastructure and operations with business outcomes.

Own the end-to-end product lifecycle for the ArrowSphere Cloud platform: define and prioritize roadmap, translate user needs into requirements, work with engineering/DevOps/UX to deliver features, integrate AI/GenAI capabilities, manage stakeholders, support go-to-market, and drive adoption through training and enablement.

Senior Product Lead to define, roadmap, and deliver Agentic AI solutions for enterprise programs. Lead requirements gathering, shape end-to-end AI/ML solutions, create PRDs and success metrics, coordinate cross-functional teams, manage stakeholders, and drive adoption, training, and value realization across brands.

Drive the full AI product lifecycle, building LLM-powered workflows, defining requirements, managing product performance, and collaborating across teams.

Act as bridge between business sponsors and AI/data engineering teams to define AI use cases, own product vision, roadmap and backlog, lead delivery of GenAI/ML solutions to production, drive adoption and ROI, manage vendors and budgets, and ensure GDPR, Responsible AI and security compliance.

Lead a team of SREs to build, automate, and maintain production-grade GPU cluster infrastructure. Design automation for server lifecycle, observability and monitoring, plan the SRE roadmap, collaborate cross-functionally, manage recruitment and careers, maintain high-availability systems, and participate in on-call rotations for fast incident resolution.

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

Lead product discovery and delivery for Doctrine's legal AI platform: understand legal users, define metrics and requirements, run experiments/MVPs, validate with users, align cross-functional teams, and continuously improve product adoption and performance.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.