Systems Engineer
US$150,000 – US$210,000 / yr base salary
Posted 13 Oct 2026
Advertised as “Senior Systems Engineer - AI Infrastructure”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
| Base salary | US$150,000 – US$210,000 / yr |
|---|
About Clockwork Systems
Clockwork.io – Software Driven Fabrics to increase GPU cluster utilization
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.
To learn more, visit www.clockwork.io.
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 You'll Do
- 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 modifying and extending them
- Build components that make large-scale GPU training more reliable and efficient
- Debug complex distributed/concurrent systems where failures are subtle and non-deterministic
- Own systems end-to-end: from design through production
What We're Looking For
Required: Systems building experience
You've designed and built complex systems—not just deployed or operated them. Examples:
- 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
Core technical skills:
- Strong C/C++ in systems contexts
- Deep understanding of concurrency, 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