Member of Technical Staff - Kernels & GPU Performance

About the role

As a Member of Technical Staff, you will build and optimize the low-level execution primitives that turn accelerator performance into production inference performance.

Rather than optimizing for one hardware architecture, you will work across accelerators with different execution models, memory hierarchies, capabilities, and software stacks. Your work will shape the latency, throughput, and efficiency Gimlet can achieve across established and emerging hardware architectures.

You will work close to the hardware across kernel implementation, memory access, execution behavior, profiling, and performance validation. You will develop optimizations that account for differences between accelerator architectures and partner with compiler, ML systems, and distributed systems engineers to improve performance across the full execution stack.

What success looks like

In the first 12-18 months, you will:

• 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

You may be a good fit if you have

• 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.

Strong candidates may also have

• 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 is a plus