Gimlet Labs is seeking a Member of Technical Staff focused on kernels and GPU performance. In this role, you will work close to accelerators and execution hardware to extract maximum performance from AI workloads across diverse and rapidly evolving platforms. You will analyze low-level execution behavior, design and optimize kernels, and ensure performance is reliable across both established and emerging hardware.
This role is ideal for engineers who enjoy deep performance work, reasoning about hardware tradeoffs, and turning theoretical peak performance into real-world results.
Responsibilities- Design, implement, and optimize GPU and accelerator kernels for AI workloads
- Analyze and tune performance across the GPU execution stack, including memory access patterns, synchronization, and instruction scheduling
- Work with compilers and runtimes to ensure kernels integrate cleanly and perform well in end-to-end systems
- Bring up and optimize execution on new or emerging accelerators
- Profile, benchmark, and debug performance issues across kernels, runtimes, and hardware
- Ensure performance optimizations are robust, correct, and production-ready at scale
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
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 is a plus
