LLM Inference Engineer
Near AI
- Location
- Remote (San Francisco, California · Remote, California)
- Employment
- Full-time
- Level
- Senior Level
About the Role
NEAR AI is building decentralized and confidential machine learning infrastructure to enable user-owned AI. They are seeking an expert in high-performance LLM serving systems and inference optimization to push the boundaries of how large language models are served.
Skills
Perks
- Remote OK
Full job details
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.