Performance Architect

About the role

The AI Storage Solutions Performance Architecture Group at Sandisk is seeking a skilled engineer to develop and optimize AI Storage Solutions based on advanced system architectures and complex simulation models. This role involves building SystemC performance models for AI Storage Solutions-based products, improving ASIC architecture performance, and collaborating with colleagues to resolve performance issues and optimize TCO.

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 technologies.
• 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.
• Collaborate 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.

Qualifications

• Minimum of a Bachelors with 7+ years experience in Performance Modeling, Simulation, and Analysis using SystemC
• Masters with 5+ years experience in Performance Modeling, Simulation, and Analysis using SystemC
• 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
• 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
• Analyzing and optimizing workloads