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.

Originally posted on