Senior Systems Engineer - AI Infrastructure

Clockwork.io•Palo Alto, CA

7h•$150,000 - $230,000

About The Position

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 . 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.

Requirements

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 (not just application code)
Deep understanding of concurrency, memory models, and failure modes
Experience reasoning about distributed system behavior: consistency, ordering, partial failures
Comfortable reading and modifying large, unfamiliar codebases
Lead design of significant system components
Navigate ambiguity and define technical direction
Mentor engineers and raise team capabilities
8+ years building systems software

Nice To Haves

GPU programming (CUDA) or GPU systems experience
High-performance networking (RDMA, InfiniBand)
ML framework or runtime internals
Cluster scheduling or orchestration systems

Responsibilities

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