AI Researcher

NVIDIA is searching for outstanding AI researchers to join the NVIDIA Research Korea team. Our mission is to advance the frontiers of foundation models and agentic AI through data-centric research.

You will join a highly collaborative research team tackling fundamental challenges in foundation models and agentic AI. Our team aims to conduct groundbreaking research, contribute to the open-source AI ecosystem, and translate research innovations into NVIDIA technologies and products.

Responsibilities

• Conduct cutting-edge research in foundation models and agentic AI, including reasoning, planning, tool use, and autonomous problem-solving.
• Develop novel methods for data-efficient learning, data curation, and scalable model training for both pre-training and post-training.
• Explore self-improvement techniques, including RL, distillation, self-training, and learning from model-generated feedback.
• Advance the capabilities and efficiency of foundation models and agentic systems through innovative pre-training, post-training, and inference-time methods.
• Develop advanced Korean-language foundation models and agentic systems, establishing methods and training recipes that generalize to other languages and regions.
• Publish original research at leading conferences and contribute to open-source research.
• Collaborate with researchers across NVIDIA and external academic institutions.
• Mentor research interns from the NVIDIA-KAIST Joint AI Research Lab and contribute to a collaborative research environment with KAIST.
• Present research findings at conferences, workshops, and other scientific events.
• Transfer research innovations to NVIDIA product teams.

Requirements

• PhD in AI, Computer Science, Electrical Engineering, or a related field (or equivalent experience).
• At least 4 years of relevant research experience.
• Strong expertise in one or more of the following areas: LLMs, pre-training, agentic AI, reasoning, reinforcement learning, efficient model training, synthetic data generation, data-efficient learning, distillation, or self-improving AI.
• Publications at leading conferences such as NeurIPS, ICML, ICLR, COLM, ACL, EMNLP, or CVPR.
• Strong understanding of modern deep learning methods and foundation models.
• Excellent programming skills in Python and experience with deep learning frameworks such as PyTorch.
• Ability to identify important research problems, develop novel approaches, and conduct rigorous empirical evaluations.
• Excellent communication skills and ability to collaborate effectively across research teams.

Nice to Have

• Research contributions to state-of-the-art foundation models, agentic systems, reasoning models, or autonomous AI.
• Experience with large-scale distributed pre-training, efficient post-training, or inference-time scaling.
• Impactful contributions to Korean-language AI, multilingual foundation models, or cross-lingual transfer.
• Significant open-source contributions to foundation models, datasets, or agentic AI frameworks.