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. We’re looking for aggressive 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.

If you are passionate about AI and how it can be applied to address real-world problems, we should talk. NVIDIA is the world leader in GPU accelerated computing and AI and is looking for developers like you to design and build enterprise AI solutions using our newest technology. 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.

What you'll be doing:

• 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, including extending pre-training and post-training capabilities to multimodal models such as VLM and VLA, by proposing state-of-the-art training and optimization frameworks.
• 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, including advances in multimodal models.

What We Need To See:

• MS/PhD in Computer Science or Engineering or equivalent experience.
• 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.
• Elite communication skills with a proven ability to articulate a value proposition to technical and non-technical audiences.

Ways To Stand Out From The Crowd:

• 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.
• Hands-on experience with multimodal model development, including VLMs, VLAs, or omni-modal models, with expertise in multimodal data processing, cross-modal alignment, or multimodal pre-training and post-training.