LLM Agent Evaluation & Evolution Intern

Bytedance; Seattle (WA); On-site; USD 58,000 - 62,000; Full time

Benefits offered by this job

Health insurance

Housing allowance

10 paid holidays per year

Paid sick time

Job summary

ByteDance offers an internship in Seattle within the Applied Machine Learning Ark team. You will help design evaluation systems for LLM-based agents, build benchmarks, and analyze logs to inform model improvements.

Interns gain hands-on experience across text and multimodal LLM pipelines, tool calling, retrieval, and multi-agent systems, contributing to MaaS platforms and data-driven decisions.

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.

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.

Skills

Python

Machine Learning

Deep Learning

LLM-based systems

Research experience

Publications

Education

Bachelor's degree in Computer Science, AI, or related field

Tools

Python ML frameworks

Retrieval

Job description

ByteDance offers an internship in Seattle within the Applied Machine Learning Ark team. You will help design evaluation systems for LLM-based agents, build benchmarks, and analyze logs to inform model improvements.

Interns gain hands-on experience across text and multimodal LLM pipelines, tool calling, retrieval, and multi-agent systems, contributing to MaaS platforms and data-driven decisions.