Research Intern - 2027 Start

Responsibilities

• Design and build large-scale container-based cluster management and orchestration systems with high performance, scalability, and resilience.
• Architect cloud-native GPU and AI accelerator infrastructure for cost-efficient and secure ML platforms.
• Deliver inference solutions using vLLM, SGLang, TensorRT-LLM, and other LLM engines.
• Stay current with Kubernetes, Ray, AI/ML, LLM infrastructure, open-source technologies, and systems research, integrating relevant practices into production systems.
• Write maintainable, testable, scalable, production-ready code.

Requirements

• Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• Able to commit to 12 weeks of work 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 large-scale cluster management systems such as Kubernetes or Ray.
• Preferred: experience with workload scheduling, GPU orchestration, scaling, or isolation in production environments.
• Preferred: experience with CUDA or inference engines such as 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.
• Preferred: strong communication and cross-functional collaboration skills.

Benefits

• 12-week internship during Summer 2027.
• Hands-on learning, community-building, and professional development events.
• Collaboration with industry experts and opportunities to contribute to products, research, future plans, and emerging technologies.
• Applications are reviewed on a rolling basis, and applicants should state their availability in their resume.