Agent Evaluation & Evolution Machine Learning Engineer Intern (AML-Ark-US) - 2027 Summer
Job details
- Employment
- Internship
- Level
- Internship
- Education
- Bachelor's degree
- Posted
- Oct 4, 2026
- Last confirmed open
- Oct 7, 2026
About this role
Public source summary from intern-list.com / Jobright.
ByteDance is seeking an intern to join its Applied Machine Learning Ark team, which develops and operates large language model service platforms and AI agent systems. The role focuses on designing evaluation systems and benchmarks for LLM-based agents, analyzing execution traces and user feedback, and supporting improvements from research through production.
Responsibilities
Design evaluation systems for LLM-based agents, covering task success, tool use, reasoning quality, and reliability Build benchmarks and automated judging pipelines, combining rule-based checks, model-based judging, and human review, etc Analyze agent execution traces and user feedback to identify failure patterns and turn them into concrete system improvements Support the closed loop from experience to capability, and work with research, platform, and product teams to bring methods into production
Qualifications: Currently pursuing a Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field Solid foundation in machine learning and deep learning Hands-on experience with LLM-based systems (e.g., agents, tool calling, retrieval, multi-agent systems) through research, internships, or projects Strong Python skills and experience with a mainstream ML or agent evaluation framework Demonstrated research or engineering ability through publications, substantial projects, internships, or open-source work Publications at top-tier ML/NLP venues (e.g., NeurIPS, ICML, ICLR, ACL etc.), especially in agent learning, self-improving/self-evolving/RSI, or agent evaluation Experience with evaluation methodology: metric design, model-based judging, or annotation and statistical analysis, etc Familiarity with LLM post-training, reasoning and planning methods, or continual learning Experience with feedback-driven optimization loops, or with large-scale log and trace analysis
Benefits: Hands-on experience and industry exposure Opportunities to apply knowledge to real-world challenges Opportunities for personal and professional growth Practical experience and opportunities to explore potential career paths Participation in social events, learning programs, and development workshops alongside industry professionals