About the job
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.
Responsibilities
Developing comprehensive validation strategies and detailed test plans covering functional, performance, power, and stress testing from silicon bring-up to product release
Executing complex test plans from RTL simulation and emulation environments through physical silicon validation
Conducting 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
Supporting production systems in AWS data centers and addressing field issues as they arise
Qualifications
Minimum
3+ years of non-internship professional software development experience
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience working with PyTorch or JAX software
Bachelor's degree in computer science, engineering, mathematics or equivalent, or experience in Java, C++, Python, or a related language
3+ years of experience with hardware performance counters and profiling tools for analyzing and optimizing system and application performance
Strong understanding of computer architecture fundamentals including memory hierarchies (caches, DRAM, HBM), compute pipelines, and interconnect topologies
Experience applying statistical methods, regression analysis, and data visualization techniques to interpret performance data and drive optimization decisions
Preferred
3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent
Experience with Machine Learning Hardware/Software Architecture
Experience with CI/CD
Experience with EDA Simulations or Emulation