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About the Role

The NEAR AI team is building decentralized and confidential machine learning infrastructure to enable user-owned AI. Our mission is to build highly scalable and efficient infrastructure for open-source AI at a global scale. We are seeking an expert in high-performance LLM serving systems and inference optimization to push the boundaries of how large language models are served.

Responsibilities

• Architect and maintain production high-traffic LLM serving systems.
• Optimize throughput, latency, and cost for leading open-source LLMs.

Requirements

• Strong hands-on experience in LLM inference, with expertise debugging and optimizing major inference engines such as SGLang, vLLM, or TensorRT.
• Deep knowledge of state-of-the-art GPU architectures, and effectively exploit them using PyTorch, Triton, CuTe, CUDA, etc.
• Proven track record in designing and maintaining end-to-end high-traffic LLM serving systems.
• Strong problem-solving skills and ability to communicate technical ideas clearly.

Skills

• LLM inference
• SGLang
• vLLM
• TensorRT
• GPU architectures
• PyTorch
• Triton
• CuTe
• CUDA
• LLM serving systems
• Trusted Execution Environments (TEE)
• Open-source LLM inference engines

Location

• San Francisco
• Remote

Work Type

• Remote

About the Company

• NEAR AI team is building decentralized and confidential machine learning infrastructure to enable user-owned AI.
• Mission is to build highly scalable and efficient infrastructure for open-source AI at a global scale.