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Member of Technical Staff - GPU Infrastructure Engineer

Liquid AI · San Francisco

CHECKED 23H AGO

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At a glance

Highlights

  • Own reliability of GPU clusters powering foundation model training.
  • Build monitoring and automation that reduce operational work for researchers.
  • Improve GPU, CPU and storage utilization across the fleet.

What they offer

  • Equity in a unicorn-stage company
  • 100% medical, dental, and vision premiums for employees and dependents
  • 401(k) matching up to 4% of base pay
  • Unlimited PTO plus company-wide Refill Days

Ideal candidate

Hands-on infrastructure engineer who brings order to complex systems and owns outcomes end-to-end. You balance urgent operations with automation and enjoy partnering with researchers to boost velocity.

Requirements

  • Strong software engineering experience building 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 supporting production users and turning recurring failures into durable solutions
  • Technical depth to partner with senior research and infrastructure engineers

Lab profile

About Liquid AI

MIT CSAIL spinout building efficient general-purpose AI for cloud to edge.

What they build

  • Liquid Foundation Models
  • LFM2.5-Audio

Known for

  • Efficiency-first foundation models
  • Data center to on-device deployment
  • Low latency with minimal memory usage
  • Liquid Neural Networks architecture

Key facts

  • Spun out of MIT CSAIL
More about Liquid AI →
Apply on the lab’s site ↗