GPU Cluster Infra Engineer - Reliability & Automation
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Benefits offered by this job
Job summary
Liquid AI is seeking a hands-on software engineer to own the reliability and operation of GPU clusters used for training and research. You will debug issues across compute, storage, networking, schedulers, and distributed workloads, while improving CPU/GPU storage utilization through tooling and automation.
You will onboard and migrate workloads across providers and hardware platforms, build monitoring and platform abstractions, and contribute to the longer-term architecture of our training
Qualifications
- Strong production-grade infra tooling and automation
- Deep knowledge of distributed systems, Linux, networking, storage
- Experience operating a shared compute cluster or distributed training platform
- Proven ability to turn recurring issues into durable solutions
- Ability to partner with senior research and infrastructure engineers
Responsibilities
- Own the reliability and operation of GPU clusters for training/research
- Debug issues across compute, storage, networking, schedulers, and distributed workloads
- Improve utilization via better tooling and automation
- Onboard and migrate workloads across GPU providers and hardware platforms
- Build monitoring, validation, and platform abstractions to reduce researcher workload
- Contribute to longer-term architecture of training infrastructure and GPU platform
Skills
Tools
Job description
Liquid AI is seeking a hands-on software engineer to own the reliability and operation of GPU clusters used for training and research. You will debug issues across compute, storage, networking, schedulers, and distributed workloads, while improving CPU/GPU storage utilization through tooling and automation.
You will onboard and migrate workloads across providers and hardware platforms, build monitoring and platform abstractions, and contribute to the longer-term architecture of our training
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