Research Intern - 2027 Start

Responsibilities

• Design and build large-scale container-based cluster management and orchestration systems.
• Architect cloud-native GPU and AI accelerator infrastructure for secure, cost-efficient ML platforms.
• Develop inference solutions using vLLM, SGLang, TensorRT-LLM, and other LLM engines.
• Integrate advances in Kubernetes, Ray, AI/ML infrastructure, LLM infrastructure, and systems research into production systems.
• Write maintainable, testable, scalable, production-ready code.
• Collaborate across teams on systems research, distributed infrastructure, and hardware acceleration.

Requirements

• Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• Available to commit to a 12-week internship during Summer 2027.
• Strong understanding of large-model inference, distributed and parallel systems, and/or high-performance networking systems.
• Hands-on experience building cloud or ML infrastructure involving resource management, scheduling, request routing, monitoring, or orchestration.
• Knowledge of Docker and Kubernetes.
• Proficiency in at least one of Go, Rust, Python, or C++.
• Preferred: experience with Kubernetes or Ray cluster management systems, GPU orchestration, workload scheduling, scaling, and production isolation.
• Preferred: experience with CUDA, vLLM, SGLang, or TensorRT-LLM.
• Preferred: familiarity with AWS, Azure, GCP, SageMaker, Azure ML, or Vertex AI.
• Preferred: knowledge of Ray, DeepSpeed, PyTorch, and distributed training or inference platforms.

Benefits

• 12-week Summer 2027 internship.
• Hands-on learning, professional development events, community-building activities, and collaboration with industry experts.
• Applications are reviewed on a rolling basis; applicants should state their start and end dates in their resume.