Tech Engagement Lead, AI Labs - EMEA

Tech Engagement Lead, AI Labs - EMEA

Nvidia

Remote, United Kingdom or France

NVIDIA is seeking a technically strong Technical Engagement Lead to work with frontier AI labs and model builders. This role will deepen the integration of NVIDIA’s platform - from GPU architecture and systems to software libraries - into leading research, training, post-training, inference, and emerging AI workloads.

You will operate at the intersection of AI research, infrastructure, product strategy, and partner engagement. You will understand how model builders develop and scale their systems, identify opportunities to improve their performance on NVIDIA, and translate those insights into product influence, technical collaborations, and joint success.

What you will be doing:

• Lead technical engagements: Build trusted relationships with senior research, engineering, and infrastructure leaders at frontier AI labs and model builders. Serve as a primary technical point of contact across NVIDIA and the partner organization;
• Understand the latest AI developments: Track frontier research, model architectures, training methods, post-training, inference systems, agents, world models, robotics, vision, and other emerging AI workloads. Translate relevant developments into NVIDIA opportunities;
• Discover and define future workloads: Identify the next workloads, use cases, and technical requirements that will shape AI infrastructure. Engage early - before architectures, interfaces, and platform decisions are fixed;
• Drive platform integration: Help partners adopt and optimize NVIDIA GPUs, systems, networking, and software libraries across their development pipelines. This may include CUDA, CUDA-X, NCCL, TensorRT-LLM, NeMo, Transformer Engine, CUTLASS, vLLM, SGLang, and related technologies;
• Influence NVIDIA’s platform roadmap: Work with NVIDIA hardware and software product teams to communicate partner requirements, identify cross-lab patterns, and influence improvements from silicon through systems, libraries, frameworks, and scalable serving;
• Support technical assessment of new AI labs: Work cross-functionally with Corporate Development, NVentures, VC Alliances, and other NVIDIA teams to assess the technical capabilities, infrastructure needs, strategic relevance, and collaboration potential of emerging AI labs;
• Develop collaboration strategies: Help define technical objectives, integration plans, milestones, success criteria, and partner-specific roadmaps. Draft technical collaboration agreements and related working documents in partnership with the appropriate NVIDIA teams;
• Celebrate joint success: Partner with Marketing, Events, Communications, and PR to identify and develop opportunities to showcase successful collaborations through GTC, developer blogs, product announcements, case studies, panels, demos, and other public or partner-facing activities.

What we need to see:

• B.S. degree or equivalent experience; an advanced degree in computer science, electrical engineering, machine learning, or a related research or technical field is preferred;
• 8+ years of experience in AI research, AI infrastructure, distributed systems, GPU computing, technical product management, or engineering;
• Current understanding of frontier AI research, model development, training, post-training, inference, and emerging AI workloads;
• Practical understanding of GPU platforms and AI software libraries, including CUDA, CUDA-X, NCCL, PyTorch or JAX, and relevant training or inference systems;
• Experience with large-scale GPU clusters, high-speed networking, distributed storage, workload orchestration, performance optimization, and cloud or on-premises infrastructure;
• Ability to understand complex model-builder architectures, identify technical bottlenecks, and translate them into actionable engineering and product requirements;
• Excellent written and verbal communication skills, including the ability to explain complex technical subjects clearly to technical and non-technical audiences;
• Ability to operate effectively across NVIDIA Product, Engineering, Sales, Marketing, Events, PR, Corporate Development, NVentures, and executive teams.

Ways to stand out from the crowd:

• Hands-on experience with large language models, multimodal models, diffusion or video models, reinforcement learning, agents, world models, robotics, or other frontier workloads;
• Experience working below the framework layer with CUDA kernels, communication libraries, compilers, memory movement, precision, or performance optimization;
• A track record of turning research or infrastructure insights into product requirements, platform improvements, technical integrations, public technical content, or joint customer success;
• Strong curiosity, sound technical judgment, and the ability to stay current as the AI landscape evolves;
• Ability to thrive in a startup mindset and dynamic environment, adapting quickly to evolving AI landscapes.