Solutions Architect, Pre-training and Post-training

At NVIDIA, we're solving the world's most challenging problems with our unique approach to accelerated computing. As a Solutions Architect, you will help researchers and developers accelerate their key workloads by using NVIDIA's 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.

If you are passionate about AI and how it can be applied to address real-world problems, we should talk. As a member of the Solution Architect team, you will work closely with customers and partners to solve hard problems in customizing and deploying AI workloads at scale.

Responsibilities:

• 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 celebrate together by presenting them at GTC.
• Continuously keep up with the latest AI training and optimization technologies that not only NVIDIA but also the community researchers provide to the market.

Requirements:

• 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.

Benefits:

• 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.