Member of Technical Staff - GPU Performance Engineer
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
Our models and workflows require performance work that generic frameworks don’t solve. You’ll design and ship custom CUDA kernels, profile at the hardware level, and integrate research ideas into production code that delivers measurable speedups in real pipelines (training, post-training, and inference). Our team is small, fast-moving, and high-ownership. We're looking for someone who finds joy in memory hierarchies, tensor cores, and profiler output.
While San Francisco and Boston are preferred, we are open to other locations.
WHAT WE'RE LOOKING FOR
We need someone who:
- Works profiler-first: You use tools like Nsight Systems / Nsight Compute to find bottlenecks, validate hypotheses, and iterate until improvements show up in end-to-end benchmarks.
- Bridges theory and practice: You can translate ideas from papers into implementations that are robust, testable, and performant.
- Executes independently: Given an ambiguous bottleneck, you can drive from profiling to kernel/integration changes to benchmarked results to maintained ownership.
- Cares about the details: Memory hierarchy, occupancy, launch configs, tensor core utilization, bandwidth vs compute limits.
THE WORK
- Write high-performance GPU kernels for our novel model architectures
- Integrate kernels into PyTorch pipelines (custom ops, extensions, dispatch, benchmarking)
- Profile and optimize training and inference workflows to eliminate bottlenecks
- Build correctness tests and numerics checks
- Build/maintain performance benchmark...