Liquid AI
Member of Technical Staff - GPU Infrastructure Engineer
San Francisco · Staff+
Sponsorship not specifiedDetected 80 days ago
Distributed SystemsKubernetesLinuxResearchHadoop
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odds of building a lasting career here
36Risky
Cap-exempt (no lottery)0
Sponsors this role74
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- This role matters because infrastructure issues can delay training by days, while improvements in utilization, storage management, and automation can significantly increase research velocity and reduce compute costs.
- You will work closely with researchers and infrastructure engineers, owning problems from immediate operational response through long-term platform improvements.
Responsibilities
- Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability.
- Our Cluster Infrastructure team owns the compute environments that power foundation model training and research at Liquid AI.
- High-impact ownership: Own infrastructure that directly affects how quickly and efficiently we train foundation models.
Requirements
- Is an engineer first: You can go deep across Linux, networking, storage, schedulers, and distributed systems.
- Deep knowledge of distributed systems, Linux, networking, and storage.
- Experience operating a shared compute cluster or distributed training platform.
- A track record of supporting production users and turning recurring failures into durable solutions.
Nice to have
- Experience with SLURM, Kubernetes, Ray, Hadoop, or another distributed compute platform.
- Experience supporting GPU, HPC, or large-scale AI training infrastructure.
- Experience with distributed storage, cluster schedulers, cloud providers, or infrastructure control planes.
- Researchers spend less time resolving infrastructure and resource-allocation issues.
- GPU, CPU, and storage resources are used more efficiently across the fleet.
- Recurring operational problems are replaced with automation, monitoring, and dependable platform tooling.
- Liquid AI has the beginnings of a durable internal platform that hides infrastructure complexity from researchers.
Compensation
- Competitive base salary with equity in a unicorn-stage company.
- We pay 100% of medical, dental, and vision premiums for employees and dependents.
- 401(k) matching up to 4% of base pay.
Benefits
- Compensation: Competitive base salary with equity in a unicorn-stage company.
- Health: We pay 100% of medical, dental, and vision premiums for employees and dependents.
- Time Off: Unlimited PTO plus company-wide Refill Days throughout the year.
Company info
- We are scaling rapidly and need exceptional people to help us get there.
- We partner with enterprises across consumer electronics, automotive, life sciences, and financial services.
- We are looking for a hands-on software engineer to keep our GPU clusters reliable, improve resource efficiency, and build the tooling that allows researchers to focus on model development rather than infrastructure.
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This listing is sourced directly from Liquid AI's careers page and normalized into a canonical job model.