Senior Deep Learning Solution Architect

As a Senior Deep Learning Solution Architect at NVIDIA, you will contribute to bringing new technology into different industries, designing AI computing platforms, and analyzing AI and HPC applications.

Key Responsibilities:

• Contribute to open-source inference frameworks like SGLang and vLLM, focusing on feature development, performance optimization, and model support.
• Develop and optimize KV cache offloading frameworks for LLM workloads to improve inference efficiency.
• Drive R&D on compute performance in distributed training and explore performance optimization methods.
• Study computational challenges in machine learning systems and build example code, acceleration libraries, or frameworks.

Requirements:

• Master's degree or above in computer science, mathematics, electrical engineering, automation, or related fields.
• Over 5 years of experience in the technology industry.
• Strong interest in accelerated computing, parallel computing, and heterogeneous computing.
• Solid programming skills and understanding of data structures and computer systems fundamentals.
• Strong learning agility, adaptability, and problem-solving skills.

Preferred Qualifications:

• Familiarity with heterogeneous computing, distributed training, parallel computing, or high-performance computing.
• Experience in performance analysis, performance modeling, or performance optimization.
• Contributions to open-source frameworks are a plus.
• Strong ability to define new problems and explore solutions.
• Proficiency with AI coding tools.

NVIDIA offers competitive salaries and a generous benefits package.