Role description
Hyperbolic LabsWho We Are
Join us in putting the world’s compute behind the people building what comes next.
At Hyperbolic, we’re making global AI compute liquid. We bring fragmented, heterogeneous GPU capacity together into one unified, productive platform, making it easier to access, sell, resell, manage, and orchestrate the resources that power AI. For neocloud, we turn GPU capacity into productive infrastructure that delivers lasting value and accelerate their time to market.
We believe bold ideas deserve the computing power to become reality. We’re building toward a future where compute flows as freely as electricity, available when and where it’s needed, so builders everywhere can turn their most ambitious ideas into breakthroughs.
We’re looking for curious, driven people who take ownership, challenge assumptions, and care deeply about what they build. If you’re energized by hard problems and want to help shape how the world accesses AI, we’d love to build with you.
About the Role
We're looking for an Inference Engineer to build inference capabilities on top of Forge, our unified control plane, so customers can consume model tokens without managing GPUs and our NeoCloud partners get a full-stack path to their own token-factory offering. You'll own how models get deployed and served across clusters distributed around the world, on heterogeneous hardware.
Deployment comes first: serving models on Forge and our Kubernetes offering, evaluating inference frameworks, and standing up the monitoring, gateways, and endpoints that make a deployment production-ready. From there the work expands into optimization, autoscaling, KV-cache orchestration, and customer inference debugging. This is the primary seat for inference at Hyperbolic \u2014 you'll build it end to end, with real influence over where the scope lands.
Who You Are
Strong general inference background with a broad, high-level command of the stack rather than a narrow specialty — you can reason about the whole path from request to token
Deep Kubernetes experience, including hands-on ability to operate clusters in production, not just deploy to them
Solid grasp of the concepts that govern inference performance: TTFT, disaggregated inference, speculative decoding, and KV cache and its inner workings
Familiarity with modern inference frameworks and serving engines, and the judgment to evaluate and select among them for a given workload
Working knowledge of NVIDIA Dynamo and how it fits into a distributed serving architecture
Experience setting up monitoring, gateways, and endpoints for production inference services
Proven ability to build a product end to end — you've taken something from nothing to serving real traffic
Strong self-initiative and comfort operating as the primary owner of an area with minimal direction
Generalist instincts: you're willing to pick up adjacent work when it's what the product needs
Preferred Qualifications
Experience spanning both inference deployment and inference optimization
Hands-on model optimization work — quantization, batching strategies, kernel-level tuning, or similar
Understanding of RDMA and high-performance networking as they apply to distributed serving
Experience deploying inference across heterogeneous accelerators
Background supporting customers directly on inference debugging and performance issues
Experience at a GPU cloud, inference provider, or AI infrastructure company
Hyperbolic is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.