Conduct in-depth research on the reasoning and generation capabilities of Large Language Models (LLMs), explore LLM reasoning techniques (such as Chain-of-Thought and multi-step reasoning), and optimize the processes and outcomes of complex creative tasks.
Build end-to-end Agent systems covering user intent recognition, knowledge retrieval, content generation and style preference alignment, to enhance user experience and personalized output capabilities.
Optimize the adaptability of models in creative scenarios through technologies such as Instruction Tuning and preference alignment (RLHF / DPO).
Conduct in-depth research on the reasoning and generation capabilities of Large Language Models (LLMs), explore LLM reasoning techniques (such as Chain-of-Thought and multi-step reasoning), and optimize the processes and outcomes of complex creative tasks.
Build end-to-end Agent systems covering user intent recognition, knowledge retrieval, content generation and style preference alignment, to enhance user experience and personalized output capabilities.
Optimize the adaptability of models in creative scenarios through technologies such as Instruction Tuning and preference alignment (RLHF / DPO).
Job Requirements
Master's degree or above in relevant fields including Computer Science, Artificial Intelligence, Natural Language Processing, Computer Vision, etc.
Proficient in deep learning frameworks (e.g., PyTorch, TensorFlow), with experience in training and optimizing Large Language Models (LLMs, e.g., GPT, LLaMA).
Familiar with content generation technologies (e.g., text generation, Text2Image, Text2Video), and proficient in Retrieval-Augmented Generation (RAG), Agent architecture design and Prompt Engineering.
Candidates who have published papers related to content generation or multimodality in top-tier conferences (e.g., ACL, EMNLP, CVPR, NeurIPS) are preferred.
Candidates with contributions to open-source projects (e.g., Stable Diffusion, DALL-E, LangChain) or independent Agent development experience are preferred.
Candidates familiar with multimodal models (e.g., GPT-4V, Flamingo) or Reinforcement Learning (RL) are preferred.
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Agent Algorithm Engineer • Santa Rosa, CA, United States
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