AI Inference Kernel Optimization Engineer

Improve the speed and serving cost of open-model inference by optimizing GPU kernels, memory use, and production runtimes. You’ll work across low-level performance engineering and deployed serving systems, with results measured in latency and token cost.

• Build and tune kernels for operations such as attention, matrix multiplication, and normalization.
• Improve data movement through cache reuse, KV-cache management, and operator fusion.
• Implement low-precision inference paths, including INT4, FP8, and INT8, for the available hardware.
• Profile GPU execution, inspect PTX or SASS where needed, and ship optimizations based on findings.
• Integrate improvements into inference runtimes and contribute appropriate changes upstream.
• Coordinate with research and platform engineers to support new model architectures.

• At least five years in systems software or ML systems engineering, including hands-on GPU performance work.
• Strong C++ and Python skills, with deep experience in at least one of CUDA, Triton, or CUTLASS.
• Ability to use hardware-level profiling tools and interpret low-level GPU execution.
• Familiarity with a modern inference runtime such as vLLM or TensorRT-LLM, and with transformer inference performance and memory behavior.
• Demonstrated kernel optimization work supported by before-and-after performance measurements.