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