Senior ML Engineer | Kimchi (Inference Stack Optimization)

Kimchi · · Full-time

Description

<h4>Why Kimchi?</h4> <p>Great models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters; today we run our own coding harness on any model you pick, and we run the models underneath it ourselves.<br><br><strong>Kimchi Inference</strong></p> <p>One API line and a customer's existing client just works: DeepSeek, GLM, Kimi, MiniMax, Nemotron served serverless from our own GPU fleet (OpenAI- and Anthropic-compatible, pay per token, spend visible per user, team, and key). When control matters, the same models run on the customer's own cluster instead. Behind that one line sits the machinery you'll own: SGLang and vLLM engines, B300 nodes, a shared cluster filesystem that holds every checkpoint exactly once, KV caches riding coding traffic with heavy prefix reuse, and the benchmark harness that gates every change we ship.<br><br></p> <p><em data-prosemirror-content-type="mark" data-prosemirror-mark-name="em"><span id="edb2bdea-40e7-48f0-9485-a0bd7169c7c7" data-mark-type="annotation" data-mark-annotation-type="inlineComment" data-id="edb2bdea-40e7-48f0-9485-a0bd7169c7…

View full original posting → Source: Arbeitnow