Research Scientist in LLM Foundation Models – Reasoning – Planning & agent

Research Scientist in LLM Foundation Models – Reasoning – Planning & agent

Requirements

• Proficiency in research experience with RL, LLM, CV and familiarity with large-scale model training
• Proficiency in data structures, and fundamental algorithm skills, fluency in C/C++ or Python
• Experience with influential projects or papers in RL, NLP, deep learning
• Excellent problem analysis and solving skills, able to deeply solve problems in large-scale model training and application
• Good communication and collaboration skills, able to explore new technologies with the team and promote technological progress.

Responsibilities

• Enhance reasoning and planning throughout the entire development process, encompassing data acquisition, model evaluation, pretraining, SFT, reward modeling, and reinforcement learning, to bolster overall performance
• Synthesize large-scale, high-quality (multi-modal) data through methods such as rewriting, augmentation, and generation to improve the abilities of foundation models in various stages (pretraining, SFT, RLHF)
• Solve complex tasks via system 2 thinking, leverage advanced decoding strategies such as MCTS, A*
• Investigate and implement robust evaluation methodologies to assess model performance at various stages, unravel the underlying mechanisms and sources of their abilities, and utilize this understanding to drive model improvements
• Teach foundation models to use tools, interact with APIs and code interpreters. Build agents and multi-agents to solve complex tasks.

Preferred Qualifications

• Proficiency in research experience with RL, LLM, CV and familiarity with large-scale model training are preferred
• Experience with influential projects or papers in RL, NLP, deep learning are preferred