ML Accelerator Performance Validation Engineer, Post Silicon Validation

Amazon Web Services (AWS); Austin (TX); On-site; USD 143,700 - 194,400; Full time

Benefits offered by this job

Sign-on payments

Restricted stock units (RSUs)

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Annapurna Labs, an AWS organization, seeks a Post‑Silicon Validation engineer to quantify ML training chip performance against targets. You will bridge silicon capabilities and real‑world workloads—ensuring accelerators deliver on latency, throughput, and efficiency at cloud scale.

You will measure, profile, and optimize across micro-architectures and full model runs, building dashboards and feeding findings back to design teams for production readiness.

Qualifications

• 3+ years of non-internship professional software development experience.
• 2+ years of non-internship design or architecture experience.
• Experience with Machine Learning fundamentals, including training/inference lifecycles, or PyTorch/JAX.
• Bachelor's degree in CS/engineering/math or equivalent; or experience in Java/C++/Python.
• 3+ years of hardware performance counters and profiling tools.
• Strong understanding of memory hierarchies, compute pipelines, and interconnect topologies.
• Experience applying statistical methods and data visualization to performance data.

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.

Skills

Software development experience

System design / architecture

Machine Learning fundamentals

Python / Java / C++

Hardware performance counters & prof.

Computer architecture fundamentals

Statistics & data visualization

CUDA kernels / ML kernels

GPU/AI accelerator experience

AllReduce / AllGather knowledge

Education

Bachelor's degree in computer science, engineering, mathematics or equivalent

Tools

CUDA kernels

Job description

Annapurna Labs, an AWS organization with development centers in the U.S. and Israel, builds custom silicon and software for AWS customers. Our team combines cloud-scale innovation with world‑class expertise across silicon engineering, hardware design, verification, software, and operations to tackle technical challenges that have never been seen before.

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 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 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.
• 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.

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