ML Accelerator Performance Validation Engineer, Post Silicon Validation

About the role

The MLA Post-Silicon Validation team owns validation of AWS's next-generation ML training accelerators from first silicon through production deployment in AWS data centers. We sit at the intersection of hardware, firmware, and ML software — ensuring every layer of the stack performs, scales, and meets the quality bar.

Responsibilities

• Design and execute performance benchmarks spanning micro-architectures to full model training
• Measure and analyze compute throughput, memory bandwidth, interconnect latency, and more
• Profile real ML workloads (transformer models, LLMs, vision models) on silicon
• Identify performance bottlenecks and work with architecture teams on optimization
• Build automated performance regression dashboards and tracking infrastructure
• Correlate silicon measurements against RTL simulation and emulation predictions

Requirements

• 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

Qualifications

• Basic Qualifications
• 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• Experience with CUDA kernels or ML/low-level kernels, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
• Knowledge of collective communications (AllReduce, AllGather) and scaling
• Experience with HBM, PCIe, and/or DMA bandwidth characterization

Skills

• Strong analytical skills with the ability to interpret complex data and make informed decisions
• Experience with machine learning frameworks such as PyTorch or JAX
• Proficiency in hardware performance analysis tools and methodologies
• Ability to work independently and as part of a team

Benefits

• Comprehensive benefits including medical, dental, vision, prescription, life insurance, AD&D, EAP, mental health support, and flexible spending accounts
• 401(k) matching program
• Paid time off and parental leave

Pay

$143,700.00 - $194,400.00 USD annually

Schedule

Full-time