Thunder Compute makes GPUs abundant with GPU virtualization research, cloud infrastructure, and enterprise partnerships that unlock more data center capacity. Backed by Y Combinator.
About the role
Your work will focus on building the cloud infrastructure surrounding our GPU virtualization layer. This includes the Go backbone of our cloud platform, Kubernetes-based orchestration, production reliability, networking, storage, billing infrastructure, and the systems used to deploy and operate GPU capacity at scale. You will take ownership of complex infrastructure from early design through production deployment. Example projects may include:
What they're looking for
- Exceptional Go ability, including concurrency, distributed systems design, API design, and production service development
- Deep understanding of Kubernetes, containers, Linux, networking, storage, or cloud infrastructure
- Experience building and operating critical production systems
- Strong systems debugging and operational ability
- Ability to reason through unfamiliar systems across multiple layers of the stack
- Working knowledge of Python, familiarity with TypeScript or Next.js is helpful
More about this role
The world is building massive amounts of GPU capacity. Meanwhile, deployed GPUs are only 20% utilized.
This is because GPUs are not virtualized, while every other type of hardware is. For example CPUs and storage are allocated through virtual abstractions which efficiently manage the physical hardware, while GPUs are statically allocated on a one-to-one basis.
Thunder Compute is building this virtualization layer for GPUs. We have raised over $17.5M from Matrix Partners, Y Combinator, and leading angels from Coreweave, Microsoft, Cognition, and Anthropic.
Leading solutions for underutilization sit at the workload layer and are therefore only able to optimize specific use cases. We believe the ideal cluster optimization solution must be invisible to developers and compatible with all workloads; hence, it must sit at the systems layer.
We are a team of systems researchers productionizing cutting-edge GPU virtualization research to build this general-purpose optimization layer.
Concretely, our virtualization library abstracts GPUs across TCP networking. We use a userspace shim library, loaded through LD_PRELOAD , to intercept CUDA calls and send them over gRPC to a host server...
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