Tech Engagement Lead

This is a fully remote position, open to applicants in France.

📋 Description

• Collaborate with senior technical leaders and research teams at AI model builders.

• Showcase and enhance NVIDIA's comprehensive accelerated computing stack for end-to-end generative AI workflows.

• Act as the main technical point of contact.

• Incorporate NVIDIA GPU architectures, DGX systems, InfiniBand, CUDA-X libraries, NeMo frameworks, and TensorRT into training and inference pipelines.

• Establish technical goals, performance advancements, and timelines in cooperation with partner AI engineering and research teams.

• Represent the software requirements of partners to NVIDIA's product and engineering teams.

• Influence product roadmap decisions based on insights gained from large-scale model training and inference environments.

• Identify cross-industry trends and advocate for enhancements to NVIDIA technologies.

• Hold regular meetings, document insights, monitor progress, and provide internal reports.

• Share techniques for developing and optimizing scalable generative AI model development pipelines.

• Keep up-to-date with NVIDIA hardware, libraries, and system updates, sharing relevant optimizations with partners.

• Promote NVIDIA GPU systems and software within designated model-builder partners.

⛳️ Requirements

• Bachelor's degree or equivalent experience.

• Over 7 years of experience in technical product or engineering roles, specializing in AI/ML, high-performance computing, or distributed systems.

• Extensive experience with platforms that support large-scale AI/ML training and inference workloads, including distributed systems, data infrastructure, and GPU cluster technologies.

• Practical knowledge of large model architectures such as Transformers and Diffusion Models.

• Familiarity with PyTorch, JAX, CUDA, cuDNN, NCCL, TensorRT, and NeMo.

• Understanding of model customization, distributed training, and inference orchestration.

• Strong knowledge of GPU cluster management, high-speed networking, parallel file systems, and both on-premise and cloud deployment.

• Insight into how large model builders operate at scale.

• Proven capability to communicate with and influence senior engineering and research leadership.

• Ability to engage with engineers, researchers, executives, and multifunctional teams.

• Hands-on experience with LLMs, diffusion models, distributed training frameworks, and advanced optimization methods.

• Knowledge of large-scale system performance optimization, Kubernetes, and Cloud Native technologies for AI workloads.

🏝️ Benefits

• Competitive salary and comprehensive benefits package.

• Opportunities for professional development and continuous learning.

• Flexible work arrangements and a supportive team environment.