Performance Architect

Sandisk understands how people and businesses consume data and we relentlessly innovate to deliver solutions that enable today’s needs and tomorrow’s next big ideas. With a rich history of groundbreaking innovations in Flash and advanced memory technologies, our solutions have become the beating heart of the digital world we’re living in and that we have the power to shape. Sandisk meets people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward. We do this through the balance of our powerhouse manufacturing capabilities and our industry-leading portfolio of products that are recognized globally for innovation, performance and quality. Sandisk has two facilities recognized by the World Economic Forum as part of the Global Lighthouse Network for advanced 4IR innovations. These facilities were also recognized as Sustainability Lighthouses for breakthroughs in efficient operations. With our global reach, we ensure the global supply chain has access to the Flash memory it needs to keep our world moving forward. In this position, you will develop AI Storage Solutions based advanced system architectures and complex simulation models for Sandisk’s next generation products.  You will need to initiate and analyze changes to the architecture of the product. Typical activities include designing, programming, debugging, and modifying simulation models to evaluate these changes and assess the performance, power, and endurance of the product. You will work closely with excellent colleague engineers, cope with complex challenges, innovate, and develop products that will change the data centric architecture paradigm.   Essential Duties and Responsibilities :    Build SystemC performance models for AI Storage Solutions based products covering end-to-end from GPU/TPU/NPU/xPU, host interface, memory hierarchy, basedie controller, and AI Storage Solutions using various packaging technolgies  Responsible for improving the AI/ML ASIC Architecture performance through hardware & software co-optimization, post-silicon performance analysis, and influencing the strategic product roadmap. Workload analysis and characterization of ASIC and competitive datacenter and AI solutions to identify opportunities for performance improvement in our products. Collaboration with Architecture team to resolve performance issues and optimize the performance and TCO of their AI Storage Solutions based datacenter technologies. Experience modeling one or some components of AI/ML accelerator ASICs such as AI Storage Solutions, PCIe/UCIe/CXL, NoC, DMA, Firmware Interactions, NAND, xPU, fabrics, etc Performance modeling and optimization for multi-trillion parameter LLM training/inference including Dense, Mixture of Experts (MoE) with multiple modalities (text, vision, speech) Model/optimize novel parallelization strategies across tensor, pipeline, context, expert and data parallel dimensions Architect memory-efficient training systems utilizing techniques like structured pruning, quantization (MX formats), continuous batching/chunked prefill, speculative decoding Incorporate and extend SOTA models such as GPT-4, Reasoning models like Deepseek-R1, and multi-modal architectures Collaborate with internal and external stakeholders/ML researchers to disseminate results and iterate at rapid pace In the AI Storage Solutions Performance Architecture Group, we build on our depth in microarchitecture expertise and simulation to analyze and optimize high-performance ASIC designs for critical areas such as AI/MLAccelerators, cloud computing, and high-performance computing. REQUIRED:    Minimum of a Bachelors with 7+ years experience in Performance Modeling, Simulation, and Analysis using SystemC  OR  Masters with 5+ years experience in Performance Modeling, Simulation, and Analysis using SystemC OR PhD with 3+ years experience in Performance Modeling, Simulation, and Analysis using SystemC At least 5+ years of experience with SystemC modeling Good understanding of computer/graphics architecture, ML, LLM Experience of simulation using System C and TLM, behavioral modeling and performance analysis  PREFERRED: Previous experience with storage systems, protocols, and NAND flash – advantage Deep experience optimizing large-scale ML systems, GPU architectures Strong track record of technical leadership in GPU performance and workload analysis Expert knowledge of transformer architectures, attention mechanisms, and model parallelism techniques Experience with GPU or TPU and system microarchitecture Proficiency in principles and methods of microarchitecture, software, and hardware relevant to performance engineering Capable of developing wide system view for complex AI/ML Accelerator ASIC systems Proficiency with SoC and system performance analysis fundamentals, tools, and techniques including hardware performance monitors and PERF profiling Familiar with IO subsystem microarchitecture performance modeling and background in NVMe/PCIe//UCIe/CXL/NVLink microarchitecture and protocols is a plus Multi-disciplinary experience, including familiarity with Firmware and ASIC design PyTorch, CUDA, TensorRT, OpenAI Triton, and ONNX Distributed systems: Ray, Megatron-LM Performance analysis tools: NSight Compute, nvprof, PyTorch Profiler KV cache optimization, Flash Attention, Mixture of Experts High-speed networking: InfiniBand, RDMA, NVLink Expertise in CUDA programming, GPU memory hierarchies, and hardware-specific optimizations Proven track record architecting distributed training systems handling large scale systems Experience with datacenter and AI workload analysis and optimization Experience with multi-core systems and multi-thread interactions Experience analyzing and optimizing workloads Sandisk is committed to providing equal opportunities to all applicants and employees and will not discriminate against any applicant or employee b