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

Requirements:

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 Py Torch 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