Member of Technical Staff - GPU Performance Engineer
Hybrid (San Francisco), Hybrid (Remote), Hybrid (Boston)Full-timePosted 11mo ago
About the role
GPU Performance Engineer focused on writing high-performance CUDA kernels, profiling at the hardware level, and shipping speedups into production pipelines for Liquid AI’s novel model architectures.
Responsibilities
- 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 benchmarks and guardrails to prevent regressions
- Collaborate closely with researchers to turn promising ideas into shipped speedups
Qualifications
- Authored custom CUDA kernels (not only calling cuDNN/cuBLAS)
- Strong understanding of GPU architecture and performance: memory hierarchy, warps, shared memory/register pressure, bandwidth vs compute limits
- Proficiency with low-level profiling (Nsight Systems/Compute) and performance methodology
- Strong C/C++ skills
Skills
Required:CUDA kernel developmentGPU architecture & performanceProfiling & performance methodologyC/C++
Nice to have:PyTorch custom opsTriton / CUTLASS (optional)
Benefits
- Unique challenges: Our architectural innovations and efficiency requirements offer unique optimization challenges. High ownership from day one.
- 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
- Financial: 401(k) matching up to 4% of base pay
- Time Off: Unlimited PTO plus company-wide Refill Days throughout the year
Last updated Jul 15, 2026. Interested? Apply on the company site.
About Liquid AI
Build efficient general-purpose AI at every scale.
We build efficient general-purpose AI at every scale. Liquid Foundation Models (LFMs) are a new generation of generative AI models that achieve state-of-the-art performance at every scale, while maintaining a smaller memory footprint and more efficient inference.
AIEdge AIFoundation ModelsGenerative AIliquid-ai-recruiting-target
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