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.

We are looking for talented individuals to join us for an internship in 2026. Internships at our Company aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities at our Company.
Internships at Our Company aims to provide students with hands-on experience in developing fundamental skills and exploring potential career paths. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth.
Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to Our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).
Start Dates
June 8th, 2026
June 22nd, 2026
July 6th, 2026

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

annually.