Staff Engineer, Product Validation Engineering ( CPU / GPU / NPU ) Performance & Benchmarking , 9-13 years, Bangalore

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.

Job Summary

We are looking for an experienced Post-Silicon Performance Measurement & Benchmarking Engineer to drive performance characterization, bottleneck analysis, and optimization of next-generation SoCs. The candidate will work closely with Architecture, Design, Firmware, Driver, Validation, and Software teams to ensure silicon achieves its targeted performance, power, and efficiency.

The candidate brings strong expertise in performance measurement methodologies, hardware performance counters, profiling tools, benchmarking, and root-cause analysis across CPU, GPU, NPU/AI accelerators, memory subsystem, and I/O.

Soft Skills

- Strong analytical and debugging skills
- Excellent problem-solving ability
- Cross-functional collaboration
- Effective communication and presentation skills
- Ability to drive performance optimization initiatives independently

Education

Bachelor’s or Master’s degree in Computer Science, Electronics, Electrical Engineering, or a related field.

Responsibilities

Develop and execute post-silicon performance validation plans.; Measure and analyze silicon performance against architectural KPIs.; Build automated benchmarking and performance characterization frameworks.; Analyze hardware performance counters (PMU/PMC) to identify bottlenecks.; Tune firmware, drivers, BIOS, OS, and platform configurations for optimal performance.; Perform workload characterization across AI, Storage, Networking, Multimedia, and Compute workloads.; Correlate pre-silicon simulation results with post-silicon measurements.; Debug CPU, Memory, Cache, Interconnect, PCIe, DDR, Storage, and AI accelerator performance issues.; Analyze bandwidth, latency, throughput, utilization, and QoS metrics.; Work with architecture teams to recommend micro-architectural improvements.; Create dashboards and performance trend reports.; Develop Python automation for benchmark execution, log collection, and report generation.; Collaborate with firmware teams to optimize scheduling, cache policies, memory allocation, and interrupt handling.; Support silicon bring-up and characterization activities.; Exposure to AI tools and methodologies

Requirements

Performance benchmarking methodologies; Throughput and latency analysis; Memory bandwidth measurement; Cache utilization analysis; Hardware counter analysis; Power-performance tradeoff analysis; Thermal throttling characterization; ARM/x86 Architecture; Cache hierarchy; DDR/LPDDR subsystem; PCIe; USB; Ethernet; NoC (Network-on-Chip); Linux perf; PMU/PMC; VTune; Perfetto; Trace32; JTAG; Lauterbach; ETM/PTM Trace; ftrace; eBPF (Preferred); Python; C/C++; Shell scripting; Linux Kernel; Android (Preferred); Embedded Linux; MLPerf; SPEC CPU; FIO; IOZone; STREAM; Geekbench; CoreMark; AI inference benchmarks; Storage performance benchmarks; Multimedia benchmarks

Nice to have

Experience with AI accelerators (NPU/DSP/GPU); Performance tuning for TensorFlow, PyTorch, ONNX Runtime, TensorRT; Understanding of compiler optimizations; Firmware optimization; Linux kernel performance tuning; DVFS and power management; Memory subsystem optimization; Storage performance optimization; Knowledge of virtualization and hypervisors