Member of Technical Staff - Kernels & GPU Performance

About the Company

Gimlet is building the next generation of AI infrastructure: large-scale AI datacenters and the orchestration platform that coordinates them. We focus on making increasingly diverse compute work together, building the orchestration layer for the future of AI infrastructure.

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

Requirements

• 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

Preferred Qualifications

• Experience with CUDA, Triton, CUTLASS, or other accelerator programming models
• Deep understanding of GPU execution models (warps/wavefronts, blocks, grids)
• Experience optimizing memory access patterns (coalescing, shared memory, cache behavior)
• Familiarity with occupancy, latency hiding, and instruction-level parallelism
• Experience using profiling and performance analysis tools
• Familiarity with multi-GPU or distributed execution