Intern - AI Systems and Infrastructure Engineering

Experience extending or optimizing LLM runtimes, serving engines, schedulers, or distributed inference frameworks.

Hands-on experience implementing advanced KV-cache, memory management, or state-management techniques for long-context or stateful AI applications.

Experience with GPU optimization technologies such as CUDA, Triton, NCCL, RDMA, or similar accelerator and communication frameworks.

Familiarity with heterogeneous memory architectures, including HBM, DRAM, CXL-attached memory, NVMe storage, pooled memory, or disaggregated memory systems.

Evidence of significant technical impact through publications, patents, open-source contributions, or substantial research and engineering projects related to Artificial Intelligence, distributed systems, memory systems, or high-performance computing.

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