Design, build, and maintain high-traffic production LLM serving systems. Optimize throughput, latency, and cost for open-source language models by tuning inference engines and GPU execution stacks. Troubleshoot and improve inference performance, and collaborate to scale efficient, reliable inference infrastructure.
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.
Near AI San Francisco, California, USA Office
535 Mission St, San Francisco, California, United States, 94105 2997
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