Level

NVIDIA

AI Research Scientist

NVIDIA is hiring an AI Research Scientist in Seoul, South Korea. Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • Level rates this research scientist role Builds AI ●●●●: the job is building AI systems.
  • The posting mentions PyTorch, Python and AI research among its tools and skills.
  • Level classes it as a senior role.
  • NVIDIA has 2,700+ other open roles on Level.
  • Posted in the last few days.

AI in this role

Conduct fundamental research in agentic AI, foundation models, and self-improving systems as an AI Research Scientist.

pytorchpython
ai-researchdeep-learningreinforcement-learningfoundation-modelssynthetic-data

NVIDIA is searching for outstanding AI researchers to join the NVIDIA Research Korea team. Our mission is to advance the frontiers of agentic AI by developing more capable, efficient, and self-improving AI systems. We focus on fundamental research in agentic reasoning, synthetic data generation, data-efficient learning, scalable training, and iterative self-improvement, exploring how AI systems can continuously improve their capabilities while reducing their dependence on expensive human supervision and computational resources.


A broader goal of our research is to advance Sovereign AI by developing generalizable methods and reproducible recipes that enable countries and language communities to build advanced AI systems without relying on massive amounts of human-annotated data. Building on NVIDIA Nemotron open models, we aim to make cutting-edge agentic AI capabilities more accessible across languages, regions, and applications.


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.


What you'll be doing:

  • Conduct cutting-edge research in agentic AI, including reasoning, planning, tool use, and autonomous problem-solving.
  • Develop novel methods for data-efficient learning, synthetic data generation, data selection, and scalable model 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 training and inference-time methods.
  • Develop advanced Korean-language AI 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 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.

What we need to see:

  • 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, agentic AI, reasoning, RL, efficient model training, synthetic data generation, data-efficient learning, RL, 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.

Ways to stand out from the crowd:

  • Research contributions to state-of-the-art agentic systems, reasoning models, or autonomous AI.
  • Experience with large-scale distributed 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.



NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and productive people in the world working for us. Looking forward to hear from you!

How we rate this

AI Research Scientist at NVIDIA rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI ResearchDeep learningReinforcement learningFoundation ModelsSynthetic DataPyTorchPython

Also common in research scientist jobs at this level

Pay

Typical research scientist job
$191k

This posting doesn't list pay. Use the typical research scientist figure when pay comes up. See research scientist jobs

Questions you could be asked

  1. Tell me about a research question you investigated. What did you find?
  2. Tell me about a project where deep learning was part of your work. What did you do?
  3. Tell me about a project where reinforcement learning was part of your work. What did you do?
  4. Tell me about a project where foundation models was part of your work. What did you do?
  5. Tell me about a project where synthetic data was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI Research, Deep learning, Reinforcement learning, Foundation Models, and Synthetic Data. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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