Senior Machine Learning Applications and Compiler Engineer

Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization. Define and implement mappings of large-scale inference workloads onto NVIDIA's systems. Extend and integrate with NVIDIA's SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms. Benchmark, profile, and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware. Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points. Prototype and evaluate new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors. Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues. Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations. Experience with large-scale AI distributed inference or training systems, including performance modeling and capacity planning for multi rack deployments.