LLM Inference Engineer

🕒 July 28

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷🏻‍♀️ Engineer

👻 Ghost score 30%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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NEAR AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2017

🤖 Artificial Intelligence

🔒 Cybersecurity

🏢 Enterprise

Artificial Intelligence • Cybersecurity • Enterprise

NEAR AI is a privacy-first AI infrastructure company that runs confidential inference and autonomous AI agents for enterprises, governments, and AI applications. Its NEAR AI Cloud and IronClaw agent framework execute models and workflows inside hardware-enforced Trusted Execution Environments (TEEs) with cryptographic isolation, zero operator access, and hardware-signed attestation for independent verification. NEAR AI offers a turnkey platform for deploying private models, automating repetitive operations, integrating with internal tools (Slack, email, Notion), and serving regulated or sovereign workloads via a marketplace of agents for finance, legal, ops, research and security.

📋 Description

• Architect and maintain production high-traffic LLM serving systems • Optimize throughput, latency, and cost for leading open-source LLMs • Push the boundaries of how large language models are served • Contribute to decentralized and confidential machine learning infrastructure enabling user-owned AI • Help build highly scalable and efficient infrastructure for open-source AI at global scale

🎯 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 • Ability to effectively exploit GPU architectures 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 • Ability to communicate technical ideas clearly • Experience with Trusted Execution Environments (TEE) preferred as a nice-to-have • Active contribution to open-source LLM inference engines preferred as a nice-to-have

🏖️ Benefits

• Interview accommodations available upon request