Member of Technical Staff - GPU Infrastructure Engineer
Liquid AI · San Francisco
CHECKED 23H AGO
Frontier labremote
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