Student Researcher - 2026 Start

Responsibilities

• Design and optimize large-scale distributed training systems, including parallelism, memory efficiency, and fault tolerance.
• Contribute to reinforcement learning training frameworks and large-scale post-training systems.
• Improve foundation-model inference performance, latency, and throughput.
• Develop compiler or runtime optimizations for heterogeneous hardware such as GPUs and accelerators.
• Perform system-level performance analysis, profiling, and bottleneck diagnosis.
• Build tooling and automation to improve developer productivity and system reliability.

Requirements

• Currently pursuing a PhD in Computer Science, Electrical Engineering, or a related technical field.
• Strong programming skills in Python and/or C++.
• Solid understanding of systems, distributed computing, machine learning systems, or performance optimization.
• Experience with distributed training frameworks, reinforcement learning training systems, GPU programming, or compiler technologies.
• Preferred: experience with large-scale ML systems or infrastructure projects, open-source ML systems or performance tooling contributions, and publications in ML systems or distributed systems.