Student Researcher (AI Foundation Models Infrastructure - Seed Infra) - 2026 Start (PhD)

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 reliability

Minimum 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)

Pay

The hourly rate range for this position in the selected city is $85–$85.

Benefits

• Day one access to health insurance, life insurance, wellbeing benefits and more
• 10 paid holidays per year
• Paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year)
• Housing allowance for interns who are not working 100% remote (eligibility may apply)