About the role
Locations: San Francisco or Remote
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 specifically seeking an expert in high-performance LLM serving systems and inference optimization. In this role, you will push the boundaries of how large language models are served.
What You'll Be Doing • Architect and maintain production high-traffic LLM serving systems. • Optimize throughput, latency, and cost for leading open-source LLMs.
What We're Looking For • 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.
We'd Love If You Have • Experience with Trusted Execution Environments (TEE). • Active contributor to open-source LLM inference engines.
Please let us know if you require any special requirements for your interview and we'll do our best to accommodate.
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