Senior Systems Engineer - AI Infrastructure

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.

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.

• →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

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

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.

• Lead design of significant system components
• Navigate ambiguity and define technical direction
• Mentor engineers and raise team capabilities
• 8+ years building systems software

• 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 - $230,000. 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.