Full-Time

Senior Systems Engineer

AI Infrastructure

Clockwork Systems

Clockwork Systems

1-10 employees

Advanced clock synchronization for distributed systems

Compensation Overview

$150k - $230k/yr

+ Equity

Palo Alto, CA, USA

In Person

Category
Software Engineering (1)
Required Skills
CUDA
PyTorch

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Requirements
  • Systems building experience
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
Desired Qualifications
  • GPU programming (CUDA) or GPU systems experience
  • High-performance networking (RDMA, InfiniBand)
  • ML framework or runtime internals
  • Cluster scheduling or orchestration systems

Clockwork Systems provides clock synchronization technology for mission-critical distributed systems, ensuring precise timing across operations. Its solutions run in both cloud and on-premises environments and are delivered through software licensing, subscriptions, and professional services. The company differentiates itself with deep timing expertise and end-to-end timing across networks, framed by tools like Latency Sensei for cloud latency monitoring. Its goal is to help customers achieve reliable, accurate synchronization to boost performance and reduce timing-related issues in time-sensitive applications.

Company Size

1-10

Company Stage

Early VC

Total Funding

$41.6M

Headquarters

Palo Alto, California

Founded

2018

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Simplify Jobs

Simplify's Take

What believers are saying

  • Raised $20.6M Series A in 2022 led by NEA for FleetIQ launch.
  • Serves hyperscalers, banks, pharma labs optimizing model training.
  • Hired NetApp exec Suresh Vasudevan as CEO in 2025.

What critics are saying

  • NVIDIA software stack bundles GPU management, blocks penetration in 12-24 months.
  • Hyperscalers like Meta, Google build proprietary fabrics in 18-36 months.
  • Customer concentration risks 30-50% revenue loss from one cloud exit.

What makes Clockwork Systems unique

  • FleetIQ delivers microsecond visibility into GPU clusters for AI workloads.
  • Software-driven fabric runs on NVIDIA, AMD, InfiniBand, RoCE, Ethernet.
  • Stanford spinout founded 2018 extends clock sync to AI training.

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Benefits

Competitive Salary

Company News

The Associated Press
Mar 11th, 2026
Clockwork.io launches TorchPass to eliminate GPU failure waste in AI training, saving $6M per 2,048-GPU cluster

Clockwork.io has launched TorchPass Workload Fault Tolerance, a software solution that eliminates costly GPU training failures through Live GPU Migration technology. The system allows AI training workloads to continue running through hardware failures, network disruptions and node crashes without requiring checkpoint restarts. The company claims TorchPass can save over $6 million annually in a typical 2,048-GPU deployment by reducing wasted training progress by 95%. In large clusters, it cuts lost time from approximately three hours per day to under ten minutes. Independent testing by SemiAnalysis found TorchPass delivered faster fault-tolerant performance than standard checkpoint-restart approaches and higher Model FLOPs Utilisation than leading open-source alternatives. The solution typically completes recovery in approximately three minutes whilst training continues uninterrupted. TorchPass is now available as part of Clockwork.io's FleetIQ platform.

The SaaS News
Sep 11th, 2025
Clockwork Raises $20.57 Million in Funding | The SaaS News

Clockwork Raises $20.57 Million in Funding

TechStartups.com
Sep 10th, 2025
Clockwork Systems Raises $20.6M for FleetIQ

Stanford spinout Clockwork has raised $20.6 million, led by NEA with participation from notable investors, to address AI's GPU inefficiency. The funding coincides with the launch of FleetIQ, a software solution aimed at enhancing GPU performance by improving communication between GPUs, clusters, and clouds. This innovation seeks to reduce crashes, shorten restarts, and increase utilization rates, making AI infrastructure more efficient and sustainable.