Job Description

The CIS Knowledge Bank team builds Prism and OneContext, the knowledge and context infrastructure that helps ByteDance business agents access reliable, fresh, and permission-aware enterprise context.
We work on Personal Profile, Personal Knowledge Bank, and Group Knowledge Bank. The team turns documents, meetings, messages, business systems, and user feedback into structured, searchable, and continuously improving knowledge. Our systems support Lark document ingestion, knowledge extraction, retrieval, permission enforcement, evaluation, and agent workflows for scenarios such as PMO weekly reports, business Q&A, and enterprise AI assistants.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.
Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.
Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.
Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

Online Assessment
Candidates who pass resume screening will be invited to participate in Our Company's technical online assessment.

Responsibilities
- Build and iterate backend services for Prism and OneContext, including knowledge ingestion, extraction, indexing, retrieval, and update workflows.
- Help transform Lark documents, meeting notes, messages, and business context into structured Knowledge Bank topics with clear provenance and access control.
- Improve retrieval quality and evaluation loops for RAG and agent workflows, including recall quality, adoption metrics, feedback processing, and observability.
- Develop internal tools, CLI/MCP interfaces, and service APIs that help agents and users query, import, and maintain knowledge safely and efficiently.
- Collaborate with product managers and engineers to turn real business scenarios into reusable Knowledge Bank capabilities.

Qualifications

Minimum Qualifications
- Currently pursuing a Undergraduate in Computer Science, Computer Engineering, Software Engineering or a related technical discipline.
- Solid computer science fundamentals, including data structures, algorithms, operating systems, databases, and distributed systems basics.
- Proficient in at least one general-purpose programming language such as Go, Python, TypeScript, Java, or C++.
- Interested in building reliable backend systems for LLM applications, RAG, information retrieval, data pipelines, or agent platforms.
- Able to understand ambiguous product requirements, break down engineering problems, and communicate clearly with cross-functional partners.

Preferred Qualifications
- Experience building evaluation, monitoring, experimentation, or feedback-loop infrastructure for AI products.
- Experience with developer tools, CLI/MCP tools, workflow automation, or AI agent frameworks.
- Strong curiosity about enterprise AI and a practical mindset for turning messy real-world context into useful product capabilities.