Tech Engagement Lead, AI Labs

Accountabilities

• Build trusted technical relationships with senior research, engineering, and infrastructure leaders at frontier AI labs and model-building organizations, serving as a primary technical point of contact across internal and partner teams.


• Track developments across frontier AI research, model architectures, training and post-training techniques, inference systems, agents, world models, robotics, vision, and other emerging workloads, translating relevant developments into actionable platform opportunities.


• Identify future AI workloads, use cases, and infrastructure requirements early, engaging with partners before key architecture, interface, and platform decisions are finalized.


• Help partners adopt and optimize GPU platforms, systems, networking, and software libraries across their development pipelines, including technologies such as CUDA, CUDA-X, NCCL, TensorRT-LLM, NeMo, Transformer Engine, CUTLASS, vLLM, SGLang, and related solutions.


• Collaborate with hardware and software product teams to communicate partner requirements, identify common needs across AI labs, and influence improvements spanning silicon, systems, libraries, frameworks, and scalable inference infrastructure.


• Support the technical assessment of emerging AI labs by evaluating their technical capabilities, infrastructure requirements, strategic relevance, and potential for future collaboration.


• Define technical collaboration strategies, integration plans, milestones, success criteria, and partner-specific roadmaps, while contributing to technical agreements and related working documentation.


• Partner with marketing, communications, events, and public relations teams to showcase successful technical collaborations through conferences, developer content, product announcements, case studies, panels, demonstrations, and other partner-facing activities.

Requirements

• Bachelor’s degree or equivalent practical experience in a technical discipline, with an advanced degree in computer science, electrical engineering, machine learning, or a related research field preferred.


• At least 8 years of experience across AI research, AI infrastructure, distributed systems, GPU computing, technical product management, engineering, or closely related areas.


• Strong current knowledge of frontier AI research, model development, training, post-training, inference, and emerging AI workloads.


• Practical experience with GPU platforms and AI software ecosystems, including CUDA, CUDA-X, NCCL, PyTorch or JAX, and relevant training or inference technologies.


• Experience working 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 technical insights into actionable engineering and product requirements.


• Excellent written and verbal communication skills, with the ability to explain sophisticated technical concepts effectively to both technical and non-technical audiences.


• Strong cross-functional collaboration skills and the ability to work effectively with product, engineering, sales, marketing, events, communications, corporate development, investment, and executive stakeholders.


• Hands-on experience with large language models, multimodal models, diffusion or video models, reinforcement learning, agents, world models, robotics, or other frontier AI workloads is highly valued.


• Experience working below the framework layer with CUDA kernels, communication libraries, compilers, memory movement, precision, or performance optimization is a strong advantage.


• Demonstrated ability to turn research or infrastructure insights into product requirements, platform improvements, technical integrations, public technical content, or successful joint initiatives.


• Strong curiosity, sound technical judgment, adaptability, and the ability to remain effective as the AI landscape and underlying technologies evolve.


• Ability to thrive in a fast-moving, startup-minded environment where priorities, technologies, and opportunities can change rapidly.

• Opportunity to work directly with leading AI research labs and model builders on some of the most advanced AI workloads in development.


• Significant technical and strategic impact through contributions to future AI infrastructure, platform capabilities, and product direction.


• Exposure to cutting-edge technologies spanning GPU computing, AI software, distributed systems, large-scale model training, inference, and emerging AI architectures.


• Highly cross-functional environment offering collaboration with engineering, product, sales, marketing, corporate development, investment, communications, and executive teams.


• Opportunity to influence the development and adoption of next-generation AI platforms and help define emerging technical requirements.


• Professional growth through high-impact technical engagements, strategic initiatives, and exposure to rapidly evolving AI technologies.


• Opportunity to contribute to industry-facing technical content, events, demonstrations, and public success stories.

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