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Salary: USD 150,000 - 210,000 / annual
Clockwork Systems, founded by Stanford researchers and veteran systems engineers, is building software-driven fabrics to increase GPU cluster utilization and improve distributed computing infrastructure. The company delivers cross-stack observability, workload fault tolerance, and performance acceleration for AI workloads.
You will 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 focused on the internals of complex infrastructure—understanding why systems break under pressure and designing solutions that handle the messy reality of distributed systems.
Key responsibilities:
- Design and implement low-level systems software for GPU clusters
- Work with internals of frameworks like PyTorch, NCCL, and CUDA runtime, modifying and extending them rather than using them as a black box
- Build components that make large-scale GPU training more reliable and efficient
- Debug complex distributed and concurrent systems where failures are subtle and non-deterministic
- Own systems end-to-end from design through production
The role offers challenging projects, a friendly and inclusive workplace culture, competitive compensation, a strong benefits package, and catered lunch.
REQUIREMENTS:
Required:
- Demonstrated systems building experience: you have designed and built complex systems (not just deployed or operated them). Examples include kernel subsystems, device drivers, OS-level components, distributed storage, databases, coordination systems, runtimes, profilers, performance tooling, network stacks, protocols, high-performance I/O systems, or large-scale infrastructure at the systems layer.
- Strong C/C++ in systems contexts
- Deep understanding of concurrency and memory models
- Experience reasoning about distributed system behavior: consistency, ordering, partial failures
- Comfortable reading and modifying large, unfamiliar codebases
Nice to have:
- GPU programming (CUDA) or GPU systems experience
- High-performance networking (RDMA, InfiniBand)
- ML framework or runtime internals
- Cluster scheduling or orchestration systems
