Job Description
Hyperbolic Labs is democratizing AI by breaking down barriers to computing power via its Open-Access AI Cloud, a GPU marketplace and AI inference service built on idle compute across the globe. This role builds inference capabilities on top of "Forge", the company's unified control plane, so customers can consume model tokens without managing GPUs.
Responsibilities
- Deploy models across globally distributed, heterogeneous-hardware clusters on Forge/Kubernetes
- Evaluate inference frameworks and set up monitoring, gateways, and endpoints
- Optimize inference through autoscaling and KV-cache orchestration
- Debug customer-facing inference issues end to end
What we are looking for
- Strong general inference background across the full request-to-token stack
- Deep hands-on Kubernetes production experience
- Solid grasp of TTFT, disaggregated inference, speculative decoding, and KV cache internals
- Working knowledge of NVIDIA Dynamo and modern inference/serving frameworks
Additional Information
Based in San Francisco, CA. Preferred experience includes inference optimization (quantization, batching, kernel tuning) and prior GPU cloud/inference-provider work.
Keywords: Kubernetes, Inference Serving Frameworks, NVIDIA Dynamo, KV-Cache Optimization, GPU Cluster Deployment, Model Quantization
Hyperbolic
📍 San Francisco, CA (Hybrid)Visit Company WebsiteHyperbolic operates a decentralized, aggregated GPU marketplace that gives AI teams affordable, on-demand access to compute. Having recently closed a Series A, the company is scaling its supply operations and infrastructure to meet growing demand for AI compute.
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