Responsibilities
Fine-tune large language models for real-world AI applications, optimizing post-training methods such as SFT, Reward Modeling (RM), and Reinforcement Learning (RL) for both training efficiency and user experience Research and develop automated high-quality data generation techniques, and build efficient online data flywheel pipelines Collaborate with engineering teams and clients to explore and deploy innovative LLM applications across domains such as content creation, education, finance, and coding Qualifications
Education & Background
Master's degree or above in Computer Science, Artificial Intelligence, Mathematics, or related fields Experience in mathematics or programming competitions is a plus Experience
Several years of experience in NLP or deep learning R&D At least 1 year of hands-on experience with LLM applications Technical Expertise
Deep understanding of the LLM technical stack, including SFT, Reward Modeling (RM), RLHF, and data synthesis Pr...