Full-Time
Technical Lead
Frontend
Posted on 7/30/2025
Clockwork Systems
1-10 employees
Advanced clock synchronization for distributed systems
No salary listed
Palo Alto, CA, USA
In Person
 Software Engineering (1) |
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- 7+ years of experience in front-end development, with at least 2 years in a technical leadership role
- Expertise in React.js/Vue.js and modern JavaScript (ES6+), TypeScript, HTML/CSS, and front-end tooling
- Experience building and shipping complex web applications in a startup environment
- Proven ability to lead and grow a high-impact engineering team
- Strong product sense and collaboration skills
- Excellent communication and decision-making ability
- Lead a team of front-end engineers, providing technical mentorship, career development, and performance feedback
- Collaborate closely with Product, Design, and Backend Engineering to build seamless, scalable, and delightful user interfaces for AI infrastructure
- Guide architectural decisions and ensure code quality, scalability, and performance across all front-end projects
- Drive frontend roadmap and sprint execution aligned with product goals
- Maintain a strong individual technical presence — you’ll be expected to code, especially in the early stage
- Hire, onboard, and grow a high-performing engineering team
- Champion best practices for testing, CI/CD, and accessibility
- Experience with Go and Python
- Familiarity with high-performance computing (HPC)/AI environments
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
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
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.
Clockwork Raises $20.57 Million in Funding
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.