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.
Responsibilities
- As an Infrastructure Intern, you may work on one or more of the following areas:
- Design and optimize large-scale distributed training systems (e.g., data/model/pipeline parallelism, memory efficiency, fault tolerance)
- Contribute to reinforcement learning training frameworks and large-scale post-training systems
- Improve inference performance, latency, and throughput for foundation models
- Develop compiler or runtime optimizations for heterogeneous hardware (GPU/accelerator)
- Work on system-level performance analysis, profiling, and bottleneck diagnosis
- Build tooling and automation to improve developer productivity and system reliabilityMinimum Qualifications:
- Currently pursuing a PhD degree in Computer Science, Electrical Engineering, or related technical fields
- Strong programming skills in Python and/or C++
- Solid understanding of systems, distributed computing, machine learning systems, or performance optimization
- Experience with one or more of the following:
- Distributed training frameworks (e.g., PyTorch FSDP, Megatron-style parallelism)
- Reinforcement learning training systems
- GPU programming (CUDA, Triton) or compiler technologies
Preferred Qualifications:
- Experience working on large-scale ML systems or infrastructure projects
- Contributions to open-source ML systems or performance tooling
- Publications in ML systems, distributed systems, or related areas (a plus but not required)
As a condition of employment, all successful candidates must be able to establish authorization to work in the United States. For this position, the Company does not provide sponsorship or any immigration-related benefits.