Student Researcher; AI Foundation Models Infrastructure - Seed Infra BS​/MS

Position: Student Researcher (AI Foundation Models Infrastructure - Seed Infra) - 2026 Start (BS/MS)
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. As a project intern, you will have the opportunity to engage in impactful short-term projects that provide you with a glimpse of professional real-world experience. You will gain practical skills through on-the-job learning in a fast-paced work environment and develop a deeper understanding of your career interests.

Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities - As an Infrastructure Intern, you may work on one or more of the following areas:

- Assist in building and optimizing large-scale distributed training systems (e.g., data/model parallelism, memory efficiency, reliability) - Support the development and improvement of reinforcement learning training pipelines and post-training systems - Improve inference performance, including latency, throughput, and system stability - Contribute to compiler or runtime optimizations for GPU and other accelerators - Conduct performance analysis, profiling, benchmarking, and bottleneck identification - Develop internal tools and automation to improve infrastructure efficiency and developer productivity - Collaborate with researchers and engineers to translate model requirements into scalable system solutions

Minimum Qualifications:

- Currently pursuing a Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related technical fields - Proficiency in at least one programming language such as Python or C++ - Familiarity with machine learning frameworks such as PyTorch or similar tools - Strong analytical and problem-solving skills - Ability to work collaboratively in a fast-paced technical environment - Interest in pursuing long-term work in ML systems or AI infrastructure