Job Description

ByteBrain is ByteDance’s AI for Infrastructure (AI4Infra) platform, dedicated to improving the efficiency, reliability, and intelligence of large-scale infrastructure systems through AI and machine learning. ByteBrain supports a wide range of infrastructure domains, including AI data center supply chains, AIOps, Operations Research and AgentOps, powering infrastructure optimization at massive scale.

Why This Role Is Unique
This role sits at the intersection of: Operations Research × AIOps × AI for Infra
You will have the opportunity to solve some of the most challenging optimization problems behind large-scale AI datacenters while pioneering the next generation of AI-powered decision-making systems, where LLMs, and optimization algorithms work together to improve efficiency, resource utilization, and operational intelligence across ByteDance's global infrastructure.

Responsibilities
- Design and develop AI, machine learning, and optimization algorithms to improve the efficiency, reliability, and performance of large-scale infrastructure systems and AI supply chain. Areas may include AIOps, operations research, software engineering, AgentOps, and system optimization.
- Drive the deployment, scaling, and continuous improvement of algorithms in production environments, supporting large-scale services.
- Identify optimization opportunities and emerging challenges from real-world infrastructure scenarios, translating them into impactful research and engineering solutions.
- Conduct cutting-edge research and publish high-quality papers in top-tier conferences and journals.

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:
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

The base salary range for this position in the selected city is $218400 - $387600 annually.