MLKernelPerformanceEngineer

Amazon Devices is an inventive research and development company that designs and engineers high-profile consumer products like the Kindle family, Fire Tablets, Fire TV, Health & Wellness devices, Amazon Echo, and Astro. We are building the next generation of edge AI capabilities through our advanced compression platform and custom neural accelerator silicon. Within Edge AI & Science, the AI Platform team builds a compression platform—the first of its kind—enabling 20-100x neural network compression for edge and cloud deployment. As model sizes grow from billions to hundreds of billions of parameters, compute efficiency becomes the single largest return on engineering investment during training. The gap between eager-mode Python and optimized GPU execution is where months of training time are won or lost. We are looking for an ML Kernel Performance Engineer to work at the hardware-software boundary of this platform, crafting high-performance CUDA and Triton kernels that make our compression algorithms run at peak efficiency during training, fine-tuning, and inference. You will build the tooling and kernel libraries that democratize GPU performance optimization across the team, enabling scientists and engineers to profile, diagnose, and fix kernel bottlenecks without needing to be CUDA experts themselves. Working alongside compression scientists and platform engineers, you will ensure that novel quantization schemes (ternary, nonary, mixed-precision) and sparse computation patterns translate into real throughput gains on GPU hardware. Your work will directly accelerate every training run in the organization and unlock deployment of compressed models to both edge devices and cloud inference. Key job responsibilities Design and implement high-performance CUDA and Triton kernels for quantization-aware training, sparse matrix operations, and low-bit inference on modern GPU accelerators Analyze and optimize kernel-level performance for compression training workloads, condu