Post-Silicon Systems Validation Engineer I, Annapurna Labs

Amazon is hiring a Post-Silicon Systems Validation Engineer I, Annapurna Labs in Austin, United States. It pays $129k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
Tools and skills the posting cites include Python, C and Rust.
Level classifies it as a junior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Validate next-generation machine learning accelerators and custom silicon infrastructure for AWS cloud computing.

PythonCRustGoSystemVerilogUVMVcsQuestaXcelium

post-silicon-validationmachine-learning-acceleratorshardware-verificationpciehbm

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 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.

Key job responsibilities
As a Validation Engineer on our Machine Learning Acceleration team, you'll own critical validation aspects across the entire product development lifecycle—from early design validation through emulation, silicon bring-up, post-silicon validation, and ongoing support of production systems deployed in AWS data centers. You'll collaborate deeply with architecture, RTL design, design verification, firmware, and software teams to ensure our next-generation AI/ML accelerators meet the highest standards of quality and performance. This role requires bridging multiple domains—from low-level hardware interfaces to high-level ML workloads—to deliver exceptional results.

We are looking for candidates with:

- Strong programming skills (Python, Lua, C/C++, Rust, Go, etc)
- A solid understanding of computer architecture
- Experience with AWS services, cloud infrastructure, firmware development (BIOS, BMC, drivers)
- Validation experience in any of these areas: PCIe, HBM, GPUs, neural networks, ML HW architecture, and/or CI/CD
- Familiarity with the validation lifecycle from RTL simulation (SystemVerilog/UVM, VCS, Questa, Xcelium) and emulation (Palladium, Zebu, Veloce) through silicon failure analysis and debug

A day in the life
- 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

Basic qualifications

- Currently has, or is in the process of obtaining a Bachelor’s or Master’s Degree in Computer Science, Computer Engineering, Data Science, Electrical Engineering, or majors relating to these fields
- Strong programming skills in two or more of: C/C++, Rust, Go, Python, Lua
- Familiarity with computer architecture (coursework or projects acceptable)
- Experience with Linux environments and Git
- Experience with system test development, code reviews, source control, build processes, or automated deployments

Preferred qualifications

- Experience with AWS services or cloud infrastructure
- Exposure to firmware development (BIOS, BMC, drivers)
- Validation experience in any of: PCIe, HBM, GPUs, neural networks, ML hardware architecture, and/or CI/CD
- Familiarity with the validation lifecycle — RTL simulation (SystemVerilog/UVM, VCS, Questa, Xcelium), emulation (Palladium, Zebu, Veloce), or silicon failure analysis and debug
- Exposure to Machine Learning hardware or software architecture (coursework or projects acceptable)

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 starting pay for this position is listed below. Starting Day 1 of employment, Amazon offers EAP, Mental Health Support, Medical Advice Line, 401(k) matching. Learn more about our benefits at https://hiring.amazon.com/why-amazon/benefits.

Austin, TX, USA - 129,000.00 USD Annually

How we rate this

Post-Silicon Systems Validation Engineer I, Annapurna Labs 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.

Classification

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.

Key facts

Post-Silicon Systems Validation Engineer I, Annapurna Labs is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $129k a year.
The role is based in US, TX, Austin.
The job was posted on July 16, 2026.
Seniority: junior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Post Silicon ValidationMachine Learning AcceleratorsHardware VerificationPcieHbmPythonCRust

Questions you could be asked

Tell me about a project where post silicon validation was part of your work. What did you do?
Tell me about a project where machine learning accelerators was part of your work. What did you do?
Tell me about a project where hardware verification was part of your work. What did you do?
Tell me about a project where pcie was part of your work. What did you do?
Tell me about a project where hbm was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Post Silicon Validation, Machine Learning Accelerators, Hardware Verification, Pcie, and Hbm. 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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