Student Researcher [LLM Post Training - Agent & Reinforcement Learning] - 2026 Start (PhD)

Descrizione del lavoro:

About the team The Seed LLM Post Training team is responsible for researching cutting-edge posttrain technologies and providing core posttrain capabilities for unified multimodal large models. The team's goal is to research and explore next-generation advanced technologies such as SFT, RM, RL, and self-learning during the posttrain phase, while significantly optimizing and improving key areas including reasoning, coding, agent, and omni model. PhD internships at ByteDance provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date). Responsibilities - Develop generalized agents capable of solving complex real-world tasks through long-horizon reasoning, memory, and multi-turn interaction. - Tackle the challenges of large-scale reinforcement learning, building systems that can scale across compute, data, and environments to improve model intelligence and alignment with human preferences. - Advance agent capabilities in long-horizon, multi-step reasoning across diverse domains, aiming to match or surpass expert-level performance. - Explore planning, tool use, and feedback mechanisms to enhance agent robustness and adaptability across domains

Requisiti del candidato:

Minimum Qualifications: - Currently pursuing a PhD in Computer Science, AI, or a related field. - Research experience in reinforcement learning, sequential decision-making, or agent behavior. - First-author publications in top-tier ML/AI conferences (e.g., NeurIPS, ICLR, ICML). - Solid programming and experimentation skills, including with RL or LLM frameworks. - Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Preferred Qualifications: - Experience with LLM agents, tool use, or prompt-based control. - Familiarity with environments such as WebArena, ALFWorld, or programmatic reasoning tasks. - Understanding of RL techniques such as reward shaping, memory augmentation, or curriculum learning