Sr. Post-Silicon Systems Software Validation Engineer, Annapurna Labs

Join our Silicon Validation team to validate next-generation machine learning accelerators that power AWS's cloud computing infrastructure. You'll work in a fast-paced, startup-like environment alongside some of the brightest minds in the industry on cutting-edge, internet-scale technology that directly impacts how customers use Machine Learning acceleration. We are changing the landscape of cloud infrastructure by accelerating the development of custom silicon by moving beyond traditional partnerships to dominate in AI training and inference.

Your work will span validation of the complete vertical stack—silicon, PCB, high-speed components (HBM, PCIe, chip-to-chip), inter-system connections, and system-to-system interfaces. You'll dive deep into new technology hardware components and scaling technologies that power our Machine Learning boards and servers at scale, ensuring every component of our hardware and software comes together into products our customers rely on.

Developing comprehensive validation strategies and leads new methodologies to improve validation coverage and time to root cause.

Role models detailed test plans covering functional, performance, power, and stress testing from silicon bring-up to product release

Mentors and provides direction to junior validation engineers

Executes complex test plans from RTL simulation and emulation environments through physical silicon validation

Handing complex debug including hands-on silicon bring-up and debug in the lab using oscilloscopes, logic analyzers, and protocol analyzers

Validating ML accelerator performance, accuracy, and reliability using real-world neural network workloads

Building test infrastructure, CI/CD, and automated regression frameworks to enable efficient validation at scale

Collaborating across architecture, design, firmware, and software teams to triage failures and drive root cause analysis to closure

Reviewing test results, identifying patterns, and providing feedback to improve design quality and validation coverage

Delivering new tests to production systems in AWS data centers and manufacturing to improve fleet health