Full-Time

Software Development Engineer in Test

Clockwork Systems

Clockwork Systems

1-10 employees

Advanced clock synchronization for distributed systems

Compensation Overview

$130k - $175k/yr

+ Equity awards

Palo Alto, CA, USA

In Person

Category
QA & Testing (1)
Required Skills
Graphics Processing Unit (GPU)
Kubernetes
Microsoft Azure
Python
Distributed Systems
GitHub Actions
PyTorch
Docker
AWS
Go
Jenkins
Terraform
Playwright
DevOps
Linux/Unix
Helm
Google Cloud Platform

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Requirements
  • At least 3 years of experience in a Test Engineering, DevOps, or Quality Assurance role with a strong technical background.
  • Strong Python skills and the ability to read and debug Go.
  • Experience building automated test scripts and frameworks from scratch.
  • Experience with continuous integration and continuous delivery tools such as GitHub Actions, Jenkins, or GitLab CI, and testing frameworks such as pytest and Playwright.
  • Experience with public cloud infrastructure such as GCP, AWS, or Azure.
  • Familiarity with release management and deployment processes, including versioning, release candidates, and rollout verification.
  • Working knowledge of Linux, containerization, and orchestration tools such as Docker, Kubernetes, and Helm.
  • Strong debugging and problem-solving skills.
  • Ability to communicate effectively.
  • Ability to work in a startup environment with self-motivation, adaptability, and ownership.
Responsibilities
  • Design, build, and maintain integration, regression, and end-to-end tests for distributed systems running on Kubernetes, Slurm, and bare-metal GPU and CPU clusters.
  • Extend the in-house end-to-end test automation framework and share ownership of the continuous integration and continuous delivery testing infrastructure and pipeline.
  • Build and run performance and scale benchmarks and identify regressions before release.
  • Automate cluster lifecycle and environment management across cloud and bare-metal environments.
  • Ensure that code releases meet quality standards before shipping to customers.
  • Collaborate with engineers to identify gaps in test coverage and build tools or frameworks to address them.
  • Investigate and diagnose build failures, flaky tests, and regressions.
  • Contribute to engineering efforts beyond Quality Assurance when appropriate, including feature development and tooling.
Desired Qualifications
  • Experience with AI/ML infrastructure, including GPUs, RDMA/RoCE or InfiniBand, NCCL, DCGM, and PyTorch training jobs.
  • Experience with workload managers and schedulers such as Slurm and Kubeflow/PyTorchJob.
  • Experience with build systems at scale such as Bazel and infrastructure-as-code tools such as Terraform.
  • Experience with chaos or fault-injection testing of distributed 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

  • Clockwork raised $20.6M in September 2025, extending runway for product expansion.
  • Oracle Cloud marketplace availability and AWS private beta broaden distribution beyond early adopters.
  • Customer proof from Uber, eBay, Wells Fargo, and RBC supports enterprise credibility.

What critics are saying

  • NVIDIA, hyperscalers, and networking vendors can bundle adjacent observability and resilience features by 2027.
  • Enterprise buyers will scrutinize YOCO credits after any migration shortfall, pressuring renewals.
  • If FleetIQ and TorchPass fail to create durable demand, Clockwork remains a niche timing vendor.

What makes Clockwork Systems unique

  • Clockwork sells software-only timing and fault-tolerance across cloud, on-prem, and hybrid clusters.
  • Its 2025-2026 stack combines Latency Sensei, Cloud Deluxe, FleetIQ, and TorchPass.
  • YOCO contracts promise 90% failure recovery with no lost training progress or recompute.

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Benefits

Competitive Salary

Company News

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
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.