[AI Research Div.] Postdoctoral Researcher - LLMs (계약직)
Salary not listed • Seoul • Contract
Posted 10mo ago
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About the job
KRAFTON AI Research is seeking a Postdoctoral Researcher to deeply understand the workings of Large Language Models (LLMs) and drive performance improvements. This role involves theoretically and empirically analyzing LLM mechanisms, proposing and validating new analytical perspectives and algorithms to enhance efficiency, stability, and performance. You will explore new LLM capabilities like tool use, agentic learning, and reasoning, validating and advancing them in interactive environments. The research will also extend to exploring LLM applications in Physical AI and World Models to create new forms of interactive intelligence. Collaboration within the division is key to translating research outcomes into actual game development and player experiences. You will lead in-depth research within your area of interest and expertise, documenting findings in Tech Reports or publishing them as papers or workshops at major AI conferences.
Responsibilities
- Theoretically and empirically analyze LLM working principles.
- Propose and validate new analytical perspectives and algorithms for LLM efficiency, stability, and performance.
- Explore and advance new LLM capabilities such as tool use, agentic learning, and reasoning in interactive environments.
- Investigate LLM applications in Physical AI and World Models.
- Collaborate with various research areas to translate research into game development and player experiences.
- Lead in-depth research within your area of interest and expertise.
- Document research findings in Tech Reports or publish them at major AI conferences.
Requirements
- PhD in AI, Computer Science, Statistics, Electrical Engineering, or a related field, or expected to obtain one before hiring.
- Experience in theoretical/empirical analysis or modeling research of Large Language Models (LLMs).
- Deep understanding of factors influencing LLM performance, including model architecture, training algorithms, and optimization techniques.
- Research and experimental experience using Python and deep learning frameworks (PyTorch, JAX, etc.).
- Quantitative analysis skills, including model performance evaluation, benchmark design, and experiment automation.
- Effective communication skills.
Benefits
- Research infrastructure for large-scale LLM training and experimentation.
- Support for external research activities such as conference participation and presentations.
- Environment for sharing expertise and growing together.
- Support for language barriers (translation, etc.) for active communication.