Developer Technology Engineer - AI

NVIDIA NVIDIA · Semiconductors · Seoul, South Korea

NVIDIA is seeking an AI Developer Technology Engineer to collaborate with developers, optimize AI workloads on GPUs, research innovative AI techniques, and ensure peak performance on GPU architectures. The role involves developing and optimizing parallel algorithms and data structures, influencing next-gen architectures, and requires proficiency in C++, AI algorithms, and specific domains like multi-modal models or RL for LLMs.

What you'd actually do

  1. Collaborating closely with key application developers to understand and address their current and future challenges. Developing and optimizing core parallel algorithms and data structures, providing top solutions with GPUs through reference code and direct app contributions.
  2. Working intimately with diverse groups at NVIDIA including architecture, research, libraries, tools, and system software teams. Your insights will influence the development of next-generation architectures, software platforms, and programming models by investigating their impact on application performance and developer efficiency.
  3. Researching and developing innovative techniques in AI. You'll conduct comprehensive analysis and optimization to ensure the best possible performance on current and next-generation GPU architectures.

Skills

Required

  • MS or PhD degree in AI computation or system optimization with a strong computational profile, or equivalent experience and 3+ years of relevant work
  • Strong knowledge of C++
  • software development
  • programming techniques
  • AI algorithms
  • Strong communication and organization skills
  • logical approach to problem solving
  • good time management
  • task prioritization skills
  • Proficiency in a specific domain, such as multi-modal model training/inference or reinforcement learning for LLMs

What the JD emphasized

  • optimize AI workloads on advanced computer architectures
  • ensure the best possible performance on current and next-generation GPU architectures
  • Proficiency in a specific domain, such as multi-modal model training/inference or reinforcement learning for LLMs

Other signals

  • optimize AI workloads on GPUs
  • researching and developing innovative techniques in AI
  • ensure the best possible performance on current and next-generation GPU architectures
  • multi-modal model training/inference or reinforcement learning for LLMs