GPGPU Software Architect/ Principal Engineer

GPGPU Software Architect/ Principal EngineerXPENGis a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing ( eVTOL ) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning , and smart connectivity.Our pioneering first-generation NPU, utilizing DSA architecture, has successfully entered mass production. We're currently validating the architecture of our second generation and are making the strategic decision to transition towards General Purpose GPU (GPGPU) architecture.We're completely overhauling our software stack and embracing the CUDA ecosystem. Our goal is to achieve over 90% compatibility with cuBLAS/cuDNN on Linux across PCIe and CXL connections, all while delivering at least 1.3 times the performance of existing solutions on Transformer and Stable-Diffusion workloads.Job Responsibilities:Software Technical StrategyDevelop and refine a comprehensive 3-year roadmap for a software stack compatible with CUDA, encompassing Runtime, Driver, Compiler, Profiler, Debugger, and AI acceleration librariesDefine binding specifications that link our upcoming GPU ISA to CUDA APIs, ensuring forward compatibility with CUDA 12.x featuresEvaluate and integrate the latest technological advancements: CUDA Graph, Transformer Engine, virtual memory management, CUDA dynamic CUTLASS 3.x, TMA, Blackwell FP4, among othersDefine the task launch protocol, including Queue, Stream, Event, and Graph, as well as the memory modelDesign a dual-mode (JIT & offline) compiler supporting LTO, PGO, Auto-Tuning, and efficient PTX→ISA microcode cachingDevelop GPU virtualization schemes(MIG) that work across processes and containersPerformance & ObservabilityBuild an observability platform: Nsys-compatible traces, real-time Metric-QPS dashboards, and an AI Advisor for identifying bottlenecks automaticallyManage internal AI benchmarks as the single source of truth. Benchmark includes MLPerf Inference, Stable Diffusion XL, and 70B LLMCross-functional CollaborationCo-design ISA which compatible with CUDA Compute Capability 12.x with our hardware architecture teamCollaborate with AI framework teams (PyTorch, TensorFlow, JAX, ONNX Runtime) to build fully reusable kernel librariesPartner with Cloud and K8s teams to co-develop Device Plugins, GPU Operators, and RDMA Network Policies10 years + in systems software, with at least 5 years in designing CUDA Compute stacksLed end-to-end development of a GPU Runtime or AI acceleration library generationComprehensive mastery of PTX/SASS, CUDA Driver API, and cuBLAS/cuDNN internals; experience with LLVM NVPTX backendProfound understanding of GPU micro-architecture, including SM architecture, Warp Scheduler, Shared-Memory conflicts, and Tensor Core pipelinesProficiency with PCIe/CXL/RDMA topologies, NUMA settings, and GPU Direct RDMA/StorageThe base salary range for this full-time position is $241,800 - $409,200 in addition to bonus, equity and benefits. 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Created: 2025-09-21