We're building a serverless LLM inference network that makes use of underutilized capacity from GPU data centers. Our product is a scheduler for running LLM inference workloads on GPUs located all over the world. We currently have over 6,000 GPUs on our network, and are growing quickly.
Things we need help with:
* Improve core scheduling algorithms
* Optimize vLLM inference runtime
* Improve logging and observability stack
* Building our user-facing dashboard and APIs
* New products
We're well-funded and have a clear path to profitability. We're currently a four person team of staff-level engineers and looking to add two more engineers to our team. Our office is near the Ferry Building in downtown San Francisco. We pay well and offer significant equity.
Apply here: https://apply.workable.com/kuzco
Or send an email to sam at kuzco.xyz with your resume, GitHub, and a bit about yourself.
Thanks!