Job Description

We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

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.

Team Introduction:
We are the business owners of the AI Compute layer and DPU at ByteDance, and the creator and open-source maintainer of AIBrix, a Kubernetes-native control plane for large-scale LLM inference. We are building the next-generation AI-native computing stack—a vertically integrated system spanning hardware acceleration, distributed orchestration, and model serving—to power some of the largest AI workloads in the world.

Our organization—AI Compute + DPU—unifies three foundational layers:
- AI Compute & Orchestration
Planet-scale, cloud-native scheduling and serverless systems built on Kubernetes, operating across hundreds of clusters with hundreds of millions of containers and jobs daily.
- Inference Infrastructure
We support Seed business workloads (including SeedDance and other ByteDance models), as well as a wide range of open-source models. Through the open-sourcing of AIBrix, we are re-architecting LLM inference around KV cache systems, multimodal serving, advanced scheduling, and disaggregated execution, pushing the frontier of performance, cost efficiency, and latency.
- DPU & Hardware-Accelerated Systems
Next-generation software–hardware co-design across compute, networking, and storage—covering GPU virtualization, RDMA/DPDK-based networking, high-speed interconnects, and distributed storage acceleration.
Together, we operate a globally distributed, hyperscale AI infrastructure powering products such as Doubao/Dola and CapCut, with a rapidly growing fleet of accelerators and increasingly deep integration with open-source ecosystems.

What Makes This Different
This is not an incremental infrastructure team.
We are redefining how AI systems are built and operated:
- Re-architecting LLM inference for heterogeneous, disaggregated environments
- Designing next-generation scheduling and caching primitives for AI workloads
- Bridging research breakthroughs with production systems at scale
- Building infrastructure that can self-optimize via AI (AI for systems)
- Driving cost-efficiency at extreme scale (GPU, CPU, network, power)
This is a system-defining opportunity to work on problems that do not yet have established solutions.

Who We Are Looking For
Through our Global Frontier Recruitment Program, we are selectively hiring a very small number of PhD candidates (graduating 2026–2027) who demonstrate the potential to: define technical directions, lead complex systems end-to-end, and create lasting impact in AI infrastructure.
We are not looking for strong candidates.
We are looking for candidates whose absence would be felt.

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 $212800 - $387600 annually.