Responsibilities

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. AI is a fast-evolving horizon — pretraining, RL, and agentic workloads each reshape the requirements faster than traditional cloud abstractions can absorb — and our team is built to keep pace rather than simply react. We approach the problem as an end-to-end AI factory: a tightly coupled production system spanning data, applications, software infrastructure, chips, energy, and the broader supply chain. In this role, you will work at the intersection of large-scale systems, AI, emerging hardware, and the cognitive foundations of intelligent agents — including next-generation AI memory systems informed by cognitive science and psychology — designing scalable architectures and driving innovations across the full AI factory stack. Responsibilities: AI Factory Architecture - Design and evaluate scalable architectures across the full AI factory — compute, storage, networking, chips, power, and the data and application layers — for large-scale training, RL, and inference workloads. Develop technical proposals for supply-chain and energy constraints alongside silicon and software trade-offs. Research & Technology Exploration - Track emerging trends across AI systems, distributed training and RL, and hardware acceleration, as well as adjacent fields such as cognitive science and psychology that inform AI memory and reasoning substrates. Build prototypes and share insights through technical reports. AI Memory & System Performance Optimization - Analyze and optimize performance across the ML stack — scheduling, networking, storage, training and RL frameworks, and emerging AI memory systems for long-horizon agents — through benchmarking and bottleneck analysis. Cross-Team Technical Alignment - Work across research, engineering, hardware, data-center, and product teams to translate AI workload requirements into scalable solutions and drive cross-team initiatives spanning the full AI factory. 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.

Qualifications

Minimum Qualifications: - Individuals who are completing or recently completed a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline. Backgrounds in cognitive science, computational neuroscience, or psychology are also welcome when paired with strong systems fundamentals. - Experience in distributed systems, infrastructure engineering, or ML systems — including exposure to large-scale training or RL pipelines — and comfort evaluating trade-offs across hardware, software, algorithms, energy, and supply-chain constraints. - Strong proficiency in integrating AI tools into knowledge discovery and research workflows. - Demonstrated ability to learn quickly and stay productive on a fast-evolving technical horizon. - Excellent communication skills to collaborate across teams. Preferred Qualifications: - Experience with large-scale model training and inference — distributed pretraining, post-training, RL, 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 training and evaluation. - Familiarity with AI memory systems, retrieval-augmented architectures, or agent long-term memory designs — bonus for exposure to cognitive-science or psychology literature on memory and reasoning. - Exposure to chip-level design, data-center energy and cooling, or AI hardware supply-chain considerations across the AI factory. - Publications in systems and/or machine learning conferences (e.g., NeurIPS, OSDI, SOSP, ASPLOS, MLSys). - Contributions to open-source projects.

Job Information