Research Scientist - AI Infra - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

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 a lean architect & research team responsible for defining the next generation of AI infrastructure at Bytedance. In this role, you will work at the intersection of large-scale systems, AI, and emerging hardware to design infrastructure that enables reliable, efficient, and scalable AI workloads. You will work closely with tech leaders, architects, and product teams to translate evolving AI requirements into robust infrastructure architectures. The role involves identifying emerging trends in AI algorithms and systems, designing scalable system architectures, and driving innovations that improve performance, reliability, and cost efficiency across the AI stack.

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.

Responsibilities:

• AI Infrastructure Architecture: Design and evaluate scalable infrastructure architectures for large-scale ML workloads across compute, storage, and networking. Develop technical proposals and specifications that guide next-generation AI infrastructure systems.
• Research & Technology Exploration: Track emerging trends in AI systems, distributed computing, and hardware acceleration. Conduct technical investigations and prototypes, and share insights through technical reports and presentations.
• Performance & System Optimization: Analyze and optimize performance across the ML infrastructure stack-including scheduling, networking, storage, and training frameworks-through benchmarking, experimentation, and bottleneck analysis.
• Cross-Team Technical Alignment: Work across research and engineering teams to translate AI workload requirements into scalable infrastructure solutions, providing architectural guidance and driving cross-team technical initiatives.Minimum Qualifications:
• Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Strong proficiency in integrating AI tools into knowledge discovery and research workflows.
• Experience in distributed systems, infrastructure engineering, or ML systems. Experienced at evaluating trade-offs across hardware, software, and algorithms.
• Excellent communication skills to collaborate across teams.

Preferred Qualifications:

• Experience with large-scale model training and inference, including distributed training, KV cache-aware serving, GPU/accelerator optimization, and high-performance networking (e.g., RDMA, NCCL).
• Experience with heterogeneous AI compute systems, large-scale training clusters, HPC-style distributed workloads, and data pipelines for large model training and evaluation.
• Publications in systems and/or machine learning conferences (e.g., NeurIPS, OSDI, SOSP, ASPLOS, MLSys).
• Contributions to open-source projects.