Research Scientist Intern - 독자 AI 파운데이션 모델

About KRAFTON AI Research Division

Since 2021, KRAFTON has been focusing on deep learning research as an area to expand our game production capabilities and continuously challenge new domains. We are actively investing in this field, including recruiting key talent.

AI Research Division Vision:

• Develop deep learning technology to aid game production, increasing efficiency and universality.
• Develop Multi-modal LLM-based Foundation Models that can play games with users.

Foundation Model R&D:

The Foundation Model Research team researches and develops core MLLM technology based on the AI Research Division's vision, contributing to creating new play experiences.

• Design and build foundation models that handle various data such as text, images, audio, and game logs.
• Accumulate theoretical and practical insights across model architecture, learning algorithms, data pipelines, and evaluation processes in large-scale distributed learning environments.

Culture Fit:

Members of the AI Research Division interact and collaborate with team members from various fields through diverse projects, presenting creative ideas for various problems. An atmosphere where opinions can be freely expressed regardless of age or position is encouraged. The team consists of members from various cultural backgrounds, and active support is provided to overcome language barriers, such as interpretation and translation, for smooth communication.

About the Role

As a Research Scientist Intern, you will:

• Conduct large-scale Multi-modal LLM training and preliminary research.
• Present research results in the form of Tech Reports or papers/workshops at top-tier conferences such as NeurIPS, ICLR, ICML.

Required Qualifications

• Master's/Ph.D. degree in a deep learning-related field or equivalent research experience.
• Experience writing papers for top-tier journals in AI/ML or top-tier conferences (NeurIPS, ICLR, ICML, etc.).
• Ability to lead research, quickly understand deep learning-related papers, and design experiments.
• Ability to adapt quickly to new domains.
• Excellent communication skills for work.
• No disqualification for overseas travel.

Preferred Qualifications

• Experience presenting or winning awards at prestigious conferences such as NeurIPS, ICML, ICLR.
• Research experience in model operating principles, such as LLM internal structure analysis, representation learning, scaling laws, and model interpretation.