Member of Technical Staff - GPU Performance Engineer
Liquid AI, San Francisco +2
About the job
About Liquid AI
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. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.
The opportunity
At Liquid AI, we’re building novel model architectures that require GPU performance engineering beyond what standard frameworks can deliver.
You’ll design, implement, and optimize custom GPU kernels that make our models faster and more efficient across training, post-training, and inference. Working closely with researchers and engineers, you’ll identify performance bottlenecks, translate architectural innovations into efficient implementations, and deliver measurable improvements in production workloads.
This is a hands-on role for someone who enjoys understanding how GPUs actually execute code, from memory access patterns and warp scheduling to tensor core utilization. You’ll have the ownership to investigate complex performance problems, make informed engineering tradeoffs, and ship optimizations that directly impact how our models perform.