Senior HPC Developer - GPU and Networking

Clockwork.io•Palo Alto, CA

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. About the Role We’re looking for a Senior HPC Developer who wants to grow in a startup environment while working on cutting-edge GPU and high-performance networking problems. You’ll work across multi-node, multi-GPU systems and have the opportunity to learn deeply across the full stack—from kernel and drivers to GPUs and networks. This role is ideal for someone who is hands-on, curious, and excited to learn by building and debugging real systems at scale.

Requirements

5+ years of experience in systems, HPC, or performance-critical software development
Strong proficiency in low-level C/C++
Solid understanding of RDMA networking, including InfiniBand, RoCE, and IBVerbs
Experience working with multi-node, multi-GPU workloads
Familiarity with collective communication libraries and communication algorithms
Ability and willingness to debug complex issues across hardware and software boundaries
Curiosity and eagerness to learn in a fast-moving startup environment

Nice To Haves

Experience with congestion control mechanisms such as DCQCN
Exposure to GPU-aware networking or advanced communication optimizations
Experience with performance profiling, tracing, or observability tooling
Background in AI infrastructure, HPC clusters, or distributed systems

Responsibilities

Build and optimize high-performance GPU and networking subsystems
Work with collective communication libraries and algorithms for multi-node, multi-GPU workloads
Debug performance issues across kernel, driver, GPU, and network layers
Develop and improve GPU-aware networking solutions
Profile, analyze, and tune system performance using low-level tooling
Collaborate closely with a small engineering team and take ownership of core systems