Tech Engagement Lead, AI Labs

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Tech Engagement Lead, AI Labs based in France.

This role sits at the intersection of frontier AI research, infrastructure, product strategy, and technical partnerships.

You will work closely with leading AI labs and model builders to understand how advanced systems are developed and scaled.

You will help optimize the adoption of GPU platforms, systems, networking, and AI software across demanding workloads.

Your insights will influence platform capabilities, product roadmaps, and future infrastructure requirements.

You will engage with emerging areas including large language models, multimodal systems, agents, robotics, inference, and world models.

The position combines deep technical expertise with strategic relationship building and cross-functional collaboration.

It offers an opportunity to shape the infrastructure behind the next generation of AI while driving measurable impact across the ecosystem.

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.

Benefits:

• 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.