Student Researcher (AI Foundation Models Infrastructure - Seed Infra) - 2026 Start (PhD)

Job Description:

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. Responsibilities - As an Infrastructure Intern, you may work on one or more of the following areas: - Design and optimize large-scale distributed training systems (e.g., data/model/pipeline parallelism, memory efficiency, fault tolerance) - Contribute to reinforcement learning training frameworks and large-scale post-training systems - Improve inference performance, latency, and throughput for foundation models - Develop compiler or runtime optimizations for heterogeneous hardware (GPU/accelerator) - Work on system-level performance analysis, profiling, and bottleneck diagnosis - Build tooling and automation to improve developer productivity and system reliability

Candidate Requirements:

Minimum Qualifications: - Currently pursuing a PhD degree in Computer Science, Electrical Engineering, or related technical fields - Strong programming skills in Python and/or C++ - Solid understanding of systems, distributed computing, machine learning systems, or performance optimization - Experience with one or more of the following: - Distributed training frameworks (e.g., PyTorch FSDP, Megatron-style parallelism) - Reinforcement learning training systems - GPU programming (CUDA, Triton) or compiler technologies Preferred Qualifications: - Experience working on large-scale ML systems or infrastructure projects - Contributions to open-source ML systems or performance tooling - Publications in ML systems, distributed systems, or related areas (a plus but not required) As a condition of employment, all successful candidates must be able to establish authorization to work in the United States. For this position, the Company does not provide sponsorship or any immigration-related benefits