Founding GPU Kernel Engineer Job at YC Work at a Startup (San Francisco)

Job Description

We're looking for a Founding GPU Kernel Engineer who lives right at the boundary between hardware and software. Someone who thinks in warps, occupancy, and memory hierarchies, and can squeeze every last FLOP out of a GPU. Your job is to go deeper than anyone else. You'll hand-tune kernels to figure out what's actually possible on the hardware, and then turn that knowledge into compiler optimization passes that help every model we compile.

Job Responsibility

Write and hand-optimize GPU kernels for ML workloads (matmuls, attention, normalization, etc.) to set the performance ceilings
Profile at the microarchitectural level: look into SM utilization, warp stalls, memory bank conflicts, register pressure, instruction throughput
Debug performance issues by digging deep into things like clock speeds, thermal throttling, driver behavior, hardware errata
Turn your hand-optimization insights into automated compiler passes (working closely with our compiler team)
Develop performance models that predict how kernels will behave across different GPU architectures
Build tools and methods for systematic kernel optimization
Work with NVIDIA, AMD, and emerging AI accelerators - understand the common parts and what's vendor-specific

Requirements

Deep expertise in GPU architecture
Proven track record of hand-writing kernels that match or beat vendor libraries (cuBLAS, cuDNN, CUTLASS)
Strong skills with low-level profiling tools: Nsight Compute, Nsight Systems, rocprof, or equivalents
Experience reading and reasoning about PTX/SASS or GPU assembly
Solid systems programming in C++ and CUDA (or ROCm/HIP)
Good understanding of how high-level ML operations map to hardware execution
Experience with distributed training systems: collective ops like all-reduce and all-gather, NCCL/RCCL, multi-node communication patterns

Nice to have

HPC background: experience with large-scale scientific computing, MPI, or work in supercomputing
Background in electrical engineering, computer architecture, or hardware design
Driver development experience (NVIDIA, AMD, or other accelerators)
Experience with MLIR, LLVM, or compiler backends
Deep knowledge of distributed ML training: gradient accumulation, activation checkpointing, pipeline/tensor parallelism, ZeRO-style optimizations
Familiarity with custom accelerators: TPUs, Trainium, Inferentia, or similar
Knowledge of high-speed interconnects: NVLink, NVSwitch, InfiniBand, RoCE
Publications or contributions in GPU optimization, HPC, or ML systems
Experience at NVIDIA, AMD, a national lab, or an AI hardware/infrastructure company

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Founding GPU Kernel Engineer

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