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Solutions Architect, Pre-training and Post-training

엔비디아(NVIDIA) · 영등포구, 서울, 한국

External listingfull-timeRecently

About The Role

At NVIDIA, we're solving the world's most challenging problems with our unique approach to accelerated computing. We're looking for passionate technologists with software and hardware. In this Solutions Architect role, you will help researchers and developers accelerating their key workloads by using NVIDIA platform. You'll define and deliver strategic partnerships, lead fruitful technical collaborations, provide first-line technical expertise and developer support, and guide NVIDIA's product strategy.

  • Create fruitful technical engagements with AI development teams in frontier model makers in Korea and lead strategic relationships with top developers and influential researchers.
  • Help them develop AI models more efficiently by proposing state-of-the-art training and optimization frameworks including Megatron-LM, Megatron-Bridge, NeMo-RL, NeMo-Gym, TensorRT Model Optimizer, and TensorRT-LLM.
  • Promote the results of the collaboration between NVIDIA and those teams with the support of marketing teams by publishing press releases and presenting at GTC.
  • Continuously keep up with the latest AI training and optimization technologies provided by NVIDIA and the broader research community.
  • 5+ years of hands-on experience in full AI model lifecycle, including pre-training, supervised fine-tuning, post-training such as reinforcement learning, optimization, and evaluation.
  • Strong software engineering skills, including debugging, performance analysis, and test development.
  • World-class communication skills with a demonstrated ability to articulate a value proposition to technical and non-technical audiences.
  • MS/PhD in Computer Science or Engineering or equivalent experience.
  • Excellent English communication skills.
  • Understanding of infrastructure factors that can affect AI model development such as GPU architecture, server block diagram, or networking bandwidth among GPU servers or between GPU servers and shared storage.

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