Student Researcher - (Seed Model - Infra - Compiler) - 2026 Start (PhD)

About the Team

The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models. Established in 2023, the ByteDance Seed team is dedicated to pioneering new paths toward artificial general intelligence. We aspire to advance the frontier of intelligence to drive progress for both technology and society. With a long-term vision for the AI sector, the Seed team's research spans MLLM, GenMedia, AI for Science, and Robotics. We maintain a global presence with laboratories and career opportunities across China, Singapore, and the United States. To date, we have launched industry-leading general foundation models and cutting-edge multimodal capabilities. Our technology powers over 50 application scenarios — including Doubao, Jimeng, TRAE, Dola and Dreamnia — and serves enterprise customers through Volcano Engine and BytePlus. Third-party data shows that the Doubao App ranks first in user volume in the Chinese market, while Doubao foundation models lead the industry in average daily token consumption.

Responsibilities

• Contribute to AI compiler optimizations for training and inference workloads
• Develop and extend MLIR-based compiler passes for graph lowering, optimization, and code generation
• Optimize model execution on GPU and NPU accelerators, focusing on performance, memory efficiency, and scalability
• Support model deployment pipelines, including compilation, packaging, and runtime integration
• Assist with distributed training and inference acceleration, such as parallel execution, communication optimization, and runtime scheduling
• Benchmark, profile, and analyze performance of large-scale models across different hardware backends
• Collaborate with researchers and engineers to translate model and system requirements into compiler and runtime improvements

Qualifications

Minimum Qualifications

• Currently pursuing a PhD degree in Computer Science, Electrical Engineering, or related technical fields
• Experience using or developing open source frameworks for LLM inference such as vLLM or SGLang.
• Proficient in at least one deep learning framework (e.g., PyTorch