Job description

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 High-performance networking (RDMA, InfiniBand) ML framework or runtime internals Cluster scheduling or orchestration systems We believe strong systems engineers pick up domain-specific tools quickly. We value your ability to reason about complex systems over checkbox familiarity with our specific stack. Enjoy Challenging projects. A friendly and inclusive workplace culture. Competitive compensation. A great benefits package. Catered lunch. Compensation for this position will vary based on the skills and experience you bring, as well as internal equity considerations. For candidates hired at the posted level, the expected base salary range is $150,000 - $210,000. In addition to cash compensation, this role is eligible to participate in the company’s equity program , which may include stock options granted in accordance with the company’s equity plan and subject to approval and applicable vesting schedules. Clockwork Systems is an equal opportunity employer. We are committed to building world-class teams by welcoming bright, passionate individuals from all backgrounds. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, religion, age, sex, sexual orientation, gender identity or expression, national origin, disability, or protected veteran status. We believe diversity drives innovation, and we grow stronger together.

Requirements

Must-haves

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
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
Strong C/C++ in systems contexts
Deep understanding of concurrency, memory models.

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
We believe strong systems engineers pick up domain-specific tools quickly. We value your ability to reason about complex systems over checkbox familiarity with our specific stack.
Challenging projects.
A friendly and inclusive workplace culture.
Competitive compensation.
A great benefits package.
Catered lunch.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision