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 solutionsMinimum 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