AmazonML Accelerator Performance Validation Engineer, Post Silicon Validation
Amazon is hiring an ML Accelerator Performance Validation Engineer, Post Silicon Validation in Austin, United States. It pays $144k-$194k a year and Level rates it ; you can apply on Amazon.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions PyTorch, JAX and Python among its tools and skills.
- Level classes it as a senior role.
- Amazon has 20,000+ other open roles on Level.
AI in this role
Validate and optimize performance metrics for AWS custom machine learning training chips against architectural targets.
Join our Post-Silicon Validation team to quantify and qualify the performance of AWS's custom ML training chips against architectural targets. You'll bridge the gap between silicon capabilities and real-world ML workload demands — ensuring our accelerators deliver on latency, throughput, and efficiency promises at cloud scale.
You'll work in a fast-paced, startup-like environment alongside some of the brightest minds in the industry on next generation AI/ML hardware that powers AWS's training and inference infrastructure. Your analysis will directly shape architectural decisions for next-generation accelerators and determine when silicon is ready for production deployment.
Key job 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
A day in the life
Your primary focus is measuring and understanding how our AI chips perform under real workloads. You'll spend mornings digging into benchmark results — figuring out where cycles are being lost and why throughput isn't hitting targets. When something looks off, you'll instrument the hardware, profile the pipeline, and work with design teams to get it fixed. Some days you'll be developing and running full training models end-to-end; others you'll be building the dashboards that tell leadership whether silicon is ready to ship.
About the team
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. Our team culture values deep technical ownership, data-driven decisions, and a bias for action. We operate with startup agility backed by AWS-scale resources, and our work directly enables the cloud computing infrastructure that millions of customers rely on for AI/ML workloads.
Basic qualifications
- 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 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
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience with CUDA kernels or ML/low-level kernels
- Knowledge of collective communications (AllReduce, AllGather) and scaling
- Experience with HBM, PCIe, and/or DMA bandwidth characterization
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, TX, Austin - 143,700.00 - 194,400.00 USD annually
How we rate this
ML Accelerator Performance Validation Engineer, Post Silicon Validation at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where performance validation was part of your work. What did you do?
- Tell me about a project where benchmarking was part of your work. What did you do?
- Tell me about a project where hardware testing was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Walk me through how you've used PyTorch in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Performance Validation, Benchmarking, Hardware Testing, Machine learning, and PyTorch. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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