Tech Engagement Lead, AI Labs - EMEA

Reposted 3 Days Ago
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Hiring Remotely in France
Remote
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Lead technical engagement with major AI model builders, integrating NVIDIA GPUs, DGX systems, networking, CUDA-X libraries, NeMo, and TensorRT into large-scale training and inference pipelines. Build strategic technical partnerships, optimize model architectures and infrastructure, influence NVIDIA product roadmaps, communicate with senior engineering and research leaders, and promote scalable generative AI best practices.
Summary Generated by Built In

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.

This role is an opportunity to help frontier AI labs build their most ambitious systems on NVIDIA, while shaping the next generation of NVIDIA’s hardware and software platform. You will help discover new workloads, influence the platform, scale technical learning across model builders, and elevate the joint successes that demonstrate what NVIDIA and its partners can achieve together.

Join NVIDIA at a crucial time as we pioneer Generative AI growth. We are in the infancy stage of building and scaling our Generative AI business for large model development. This role offers a unique opportunity to join this rapid expansion. NVIDIA's hardware, systems, and software libraries are at the heart of this growth. They empower large model builders to revolutionize their operations with powerful AI capabilities. This is your chance to be a key member of a team that will shape the future of AI model development, working with the world's leading AI research labs and the most innovative technologies. Your contributions will directly impact the trajectory of our Generative AI success, making this an unparalleled opportunity for professional growth and significant impact.

Skills Required

  • B.S. degree or equivalent experience
  • 7+ years of experience in technical product or engineering roles focused on AI/ML, high-performance computing, or distributed systems
  • Experience with large-scale AI/ML training and inference platforms, distributed systems, data infrastructure, and GPU cluster technologies
  • Hands-on knowledge of large model architectures, including Transformers and Diffusion Models
  • Familiarity with PyTorch, JAX, CUDA, cuDNN, NCCL, TensorRT, and NeMo
  • Understanding of model customization, distributed training, and inference orchestration
  • Understanding of GPU cluster management, high-speed networking, parallel file systems, and on-premises and cloud infrastructure
  • Ability to communicate with and influence senior engineering, research, and executive leaders
  • Ability to collaborate effectively with engineers, researchers, executives, and cross-functional teams
  • Hands-on experience with large language models, diffusion models, distributed training frameworks, and advanced optimization techniques
  • Experience prototyping and integrating AI technologies into model development pipelines
  • Understanding of large-scale system performance optimization, Kubernetes, and cloud-native technologies

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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