Member of Technical Staff - Kernels & GPU Performance
San Francisco, CA$150k - $390kFull-timePosted 3mo ago
About the role
Role focused on building and optimizing low-level execution primitives to convert accelerator performance into production AI inference performance across multiple hardware architectures.
Responsibilities
- Build and optimize kernels that improve latency, throughput, and hardware utilization for production AI workloads
- Develop execution strategies that unlock performance across both established and emerging accelerator architectures
- Improve memory efficiency, scheduling behavior, and execution characteristics across the inference stack
- Partner with compiler, runtime, and distributed systems engineers to ensure end-to-end performance optimization
- Influence how heterogeneous hardware is deployed and utilized within the next generation of AI infrastructure
- Help establish performance engineering standards that shape the future of Gimlet's execution platform
Qualifications
- Strong software engineering fundamentals
- Experience working on performance-critical systems close to hardware
- Comfort reasoning about low-level execution behavior, memory hierarchies, and performance tradeoffs
- Bachelor's degree in a relevant field, or an equivalent combination of education, training, and professional experience.
Skills
Required:Software engineering fundamentalsPerformance optimization near hardwareLow-level execution modeling
Nice to have:CUDA / Triton / CUTLASS (plus)GPU execution modelsMemory access optimizationProfiling and performance analysis tools
Last updated Oct 11, 2026. Interested? Apply on the company site.
About Gimlet Labs
Gimlet Labs
Gimlet Labs is an applied research lab focused on building high performance AI systems.
Autonomous kernel generation for heterogeneous hardwareCost-aware optimization frameworks for AI workloadsGimlet cloudHeadless hardware architectures for serving AI inferenceHybrid edge/cloud workload partitioning and orchestrationSLA-aware dynamic datacenter scheduling of AI agent workloadsUniversal AI compiler for heterogeneous hardwarebessemer-ai-infrastructure-2026
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