Research Scientist - Driven Agent Self-Evolution - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

Summary: ByteDance is a technology company with a mission to inspire creativity and enrich life. They are seeking a Research Scientist to develop agent frameworks that continuously learn and improve from execution traces, user feedback, and environmental signals, while collaborating across teams to turn research ideas into production-grade systems. Responsibilities: - Research and develop agent frameworks that continuously learn and improve from execution traces, user feedback, and environmental signals - Build large-scale log analytics pipelines to extract quality signals, usage patterns, and actionable insights from model and agent invocation logs, driving data-informed system and model improvements - Explore and apply frontier techniques in LLM post-training, reasoning, and planning to enhance agent capabilities - Collaborate across algorithm research, platform engineering, and product teams to turn research ideas into production-grade systems at scale Required Qualifications: - Individuals who are completing or have recently completed a Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related discipline - Strong theoretical and practical foundation in machine learning, deep learning, reinforcement learning, or optimization - Research experience in at least one of the following areas: LLM-based agents, planning and reasoning, multi-agent systems, continual/lifelong learning, or LLM post-training (e.g., RLHF, DPO, GRPO, self-play) - Strong programming skills in Python and proficiency with ML frameworks (e.g., PyTorch, TensorFlow, JAX) - Publication record at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, AAMAS, COLM) - Strong problem-solving skills and ability to thrive in a fast-paced, collaborative environment Preferred Qualifications: - Publications in areas directly related to agent learning and adaptation, such as tool use, self-improvement, skill discovery, trajectory optimization, reward modeling, or agent evaluation - Research experience in LLM reasoning and planning, including chain-of-thought, tree/graph search, Monte Carlo methods, or inference-time compute scaling - Experience training or fine-tuning large language models, including supervised fine-tuning, preference optimization, or curriculum learning - Hands-on experience building or evaluating LLM-based agent systems (e.g., ReAct, function calling, code generation agents, or multi-agent orchestration) - Familiarity with meta-learning, few-shot generalization, or transfer learning in the context of LLM-based systems - Experience with feedback-driven optimization loops, such as online learning, bandit methods, or evolutionary strategies applied to agent improvement - Strong interest in bridging frontier AI research with production-grade engineering — turning papers into systems that work at scale - Internship experience at technology companies or research organizations Required Skills: Machine Learning, Deep Learning, Reinforcement Learning, Optimization, Large Language Models, LLM Post-Training, Python, PyTorch, TensorFlow, JAX, Multi-Agent Systems, Planning and Reasoning, Log Analytics, Model Fine-Tuning, Preference Optimization, Curriculum Learning, Meta-Learning, Few-Shot Generalization, Transfer Learning, Online Learning, Bandit Methods, Evolutionary Strategies Benefits: Medical, dental, and vision insurance, 401(k) savings plan with company match, Paid parental leave, Short-term and long-term disability coverage, Life insurance, Wellbeing benefits, 10 paid holidays per year, 10 paid sick days per year, 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)