Research Scientist Graduate (Multi-modal Agentic Databases) - 2027 Start (PhD)
ByteDance, Seattle, Washington
About the job
About the team
With the fast growth of Bytedance's business, Bytedance's system infrastructure is currently at a massive scale and requires versatile system solutions. With the large-scale deployment of large language models (LLMs) and AI Agents, traditional cloud-native infrastructure can no longer meet the extreme performance and elasticity demands of AI workloads. Our team works on advanced R&D projects in building multi-modal agentic database infrastructure, focusing on:
- Multi-modal query processing: Support seamlessly integrated multi-modal query processing, including vector, full-text and regular SQL query processing over various typed data. In addition, how to support large scale semantic operators in a cost-effective and low-latency way is also our research focus.
Intelligence & Agent Architecture
- Explore infrastructure auto-optimization based on AI Agent workflows. Build a self-evolving business Agent framework, enabling full-stack intelligent optimization through “AI for Infrastructure”. We look into various ways to apply AI/LLM in solving infrastructure problems, such as NL-to-SQL, Auto Skills etc. This project aims to build next-generation AI-native infrastructure to support LLMs and AI Agents, improving resource utilization, reducing costs, enabling elastic scalability, and driving the evolution of AI infrastructure technologies.