Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Deep familiarity with transformer and attention kernel design: FlashAttention-class algorithms, KV-cache management, paged and chunked attention, linear and state-space attention variants, MoE routing and expert dispatch 8+ years of experience in accelerator software, HPC, or ML systems performance (or equivalent with an advanced degree) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience with compiler and codegen technologies relevant to kernels: MLIR, LLVM, TVM, XLA, Halide, or polyhedral scheduling Experience with low-precision numerics and quantization — FP8/E4M3/E5M2, MX and other block-scaled formats, INT8/INT4 — including error analysis and calibration Experience mentoring engineers and setting technical direction across teams Track record of open-source contribution in the kernel, compiler, or ML systems ecosystem Experience with distributed execution and collective communication (NCCL/RCCL-class primitives, tensor and expert parallelism, overlapping communication with compute) Experience building or contributing to high-performance kernel libraries or frameworks — CUTLASS, cuBLAS, cuDNN, CUTE, Triton, Helion, ThunderKittens, oneDNN, Composable Kernel, or comparable internal equivalents Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience with pre-silicon software development — architectural simulators, FPGA emulation, performance modeling — and with hardware/software co-design cycles Familiarity with ML framework internals: PyTorch dispatch and eager execution, torch.compile / Inductor, custom operator integration, and inference serving stacks such as vLLM or SGLang
$154,003/year to $217,000/year + bonus + equity + benefits