Backed by NEA.
About the role
We're building infrastructure for fault-tolerant, high-performance distributed GPU training. You'll work at the intersection of GPU systems, high-speed networking, and distributed coordination—designing and implementing systems that run at scale. This is a systems building role. You'll dig into internals, understand why things break under pressure, and design solutions that handle the messy reality of distributed systems.
What they're looking for
- Kernel subsystems, device drivers, or OS-level components
- Distributed storage, databases, or coordination systems
- Runtimes, profilers, or performance tooling
- Network stacks, protocols, or high-performance I/O systems
- Large-scale infrastructure at the systems layer
- Strong C/C++ in systems contexts (not just application code)
More about this role
Clockwork Systems was founded by Stanford researchers and veteran systems engineers who share a vision for redefining the foundations of distributed computing. As AI workloads grow increasingly complex, traditional infrastructure struggles to meet the demands of performance, reliability, and precise coordination. Clockwork is pioneering a software-driven approach to AI fabrics by delivering cross-stack observability to catch and quickly resolve problems, workload fault tolerance to keep jobs running through failures, and performance acceleration that dynamically routes and paces traffic to avoid congestion.
We're building infrastructure for fault-tolerant, high-performance distributed GPU training. You'll work at the intersection of GPU systems, high-speed networking, and distributed coordination—designing and implementing systems that run at scale.
This is a systems building role. You'll dig into internals, understand why things break under pressure, and design solutions that handle the messy reality of distributed systems.
- Design and implement low-level systems software for GPU clusters
- Work with internals of frameworks like PyTorch, NCCL, CUDA runtime—not as a user, but...
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