GPU Cluster Infrastructure Engineer

PVH (Tommy Hilfiger/Calvin Klein); United States; On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Equity in unicorn-stage company

100% premiums covered for medical, den

401(k) matching up to 4%

Unlimited PTO

Refill Days

Job summary

Liquid AI is seeking a hands-on software engineer to own the reliability of GPU clusters used for foundation model training and research, and to build tooling that increases resource utilization and reduces toil for researchers.

You will work closely with researchers and infrastructure engineers, addressing issues from immediate operational response to long‑term platform improvements, with a strong emphasis on automation and monitoring.

Qualifications

• Strong software engineering experience with production-quality infrastructure tooling and automation.
• Deep knowledge of distributed systems, Linux, networking, and storage.
• Experience operating a shared compute cluster or distributed training platform.
• Track record of supporting production users and turning recurring failures into durable solutions.

Responsibilities

• Own the reliability and operation of the GPU clusters used for training and research.
• Debug issues across compute, storage, networking, schedulers, and distributed workloads.
• Improve CPU, GPU, and storage utilization through better tooling and automation.
• Onboard and migrate workloads across GPU providers and hardware platforms.
• Build monitoring, validation, and platform abstractions that reduce operational work for researchers.
• Contribute to the longer‑term architecture of Liquid AI's training infrastructure and GPU platform.

Skills

Software engineering

Distributed systems

Linux

Networking

Storage

Automation

Production users support

Problem solving

Tools

SLURM

Kubernetes

Ray

Hadoop

Distributed storage

Cloud providers

Job description

Liquid AI is seeking a hands-on software engineer to own the reliability of GPU clusters used for foundation model training and research, and to build tooling that increases resource utilization and reduces toil for researchers.

You will work closely with researchers and infrastructure engineers, addressing issues from immediate operational response to long‑term platform improvements, with a strong emphasis on automation and monitoring.