Infrastructure Research Intern - ML Systems

ByteDance; Seattle (WA); On-site; USD 28,000 - 39,000; Part time

Job summary

ByteDance is seeking an Infrastructure Intern in Seattle to join the Seed team’s technology initiative. You will contribute to large-scale distributed training, reinforcement learning frameworks, and performance optimization for foundation models.

The role requires pursuing a PhD in CS/EE with strong Python/C++ skills and experience in distributed systems or ML tooling. Work authorization in the United States is required; sponsorship is not provided.

Qualifications

• Currently pursuing a PhD in Computer Science, Electrical Engineering, or related technical fields.
• Strong programming skills in Python and/or C++.
• Solid understanding of systems, distributed computing, ML systems, or performance optimization.
• Experience with distributed training frameworks and RL training systems.
• GPU programming (CUDA, Triton) or compiler technologies.

Responsibilities

• Design and optimize large-scale distributed training systems (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.

Skills

Python

C++

Distributed computing

Machine learning systems

Performance optimization

CUDA/Triton

Education

PhD candidate in CS/EE

Tools

Megatron-style parallelism

PyTorch FSDP

Job description

ByteDance is seeking an Infrastructure Intern in Seattle to join the Seed team’s technology initiative. You will contribute to large-scale distributed training, reinforcement learning frameworks, and performance optimization for foundation models.

The role requires pursuing a PhD in CS/EE with strong Python/C++ skills and experience in distributed systems or ML tooling. Work authorization in the United States is required; sponsorship is not provided.