The infra-compute division focuses on building large-scale, highly available Cloud and AI infrastructure.
Our work powers both ByteDance’s public cloud offerings and its internal corporate products.
The US team is dedicated to the research and development of cutting-edge technologies, including training, inference, and AI Agent infrastructure.
Responsibilities
Develop key technologies to optimize our AI Infra stack, including training infra, inference infra, and AI agents.
Work with academia and open source communities on joint development.
Follow and research the latest technologies from academia or industry and conduct deep-dive analysis.
Present our research and products in academic papers.
Topic Content
With the large-scale adoption of LLMs and AI agents, traditional cloud-native infrastructure can no longer meet the ultra-high performance and elasticity requirements of AI workloads.
This topic conducts systematic research across the entire AI infrastructure stack:
Network and Observability: Research intelligent fault localization and root cause analysis for large-scale AI clusters, combined with intelligent tuning of time-series databases to improve cluster stability.
Storage Systems: Develop serverless high-performance elastic file systems and storage acceleration architectures specifically for AI scenarios, explore hardware-software co-optimization for DPU, and overcome AI storage performance bottlenecks.
Data Center Power Scheduling: Research GPU/CPU/MEM heterogeneous collaborative scheduling technologies, build a heterogeneous power orchestration system for AI agents, and address scheduling challenges including heterogenous workloads and state dependencies.
Vector Retrieval: Optimize core vector retrieval technologies for LLM-powered applications, building a cloud-native distributed vector index engine to meet ultra-large-scale vector retrieval demands with low latency and low cost.
Intelligence and Agent Architecture: Explore automatic infrastructure optimization based on AI Agent workflows, build a self-evolvable business agent framework, and enable full-stack intelligent optimization through AI for Infra.
This topic aims to build a next-generation AI-native infrastructure to support the deployment of LLMs and AI agents, improve resource utilization, reduce costs, support elastic scaling, and drive the technological evolution of AI infrastructure.
Requirements
Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, Artificial Intelligence or a related technical discipline.
Experience with at least one of the following areas
AI Agents and Agent Infra, including coding agents, general-purpose agents, agent memory, agent sandbox, etc.
Commit to proactive continuous learning, demonstrate enthusiasm for AI technologies, and exhibit a strong ability to quickly grasp and apply new technologies.
Good communication and teamwork skills.
Preferred
LLM training infra, including optimizations for various post-training workloads such as RL training, knowledge distillation, etc.
LLM inference infra, including inference engine performance improvements, more efficient execution parallelism, GPU kernel optimizations, etc.
Frequent contributors or maintainers in AI Infra related open source communities such as PyTorch, vLLM or SGLang.
Having publications in CS conferences such as OSDI or MLSys.
Eligibility and application
Successful candidates must be able to commit to an onboarding date by end of year 2027.
Please state your availability and graduation date clearly in your resume.
