Software Development Engineer II, Silicon Scan & Fleet Diagnostics, Machine Learning Accelerator Technology Team
Amazon · On-site · Full-time · Austin, Texas, USA · United States
Posted (17h ago)
- Employer posted
- First seen here
- Repost check
- Same job ID since we first saw it on Oct 13, 2026. If Amazon reposts it, we keep the original posting date.
- Level
- Mid-level
- Work setup
- On-site
- Tech mentioned
- AWS
- Amazon hiring
- 821 roles posted in the last 30 days
Job description
Annapurna Labs (our organization within AWS Utility Computing (UC)) designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago—even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world.
In Annapurna Labs we are at the forefront of hardware/software co-design not just in Amazon Web Services (AWS) but across the industry. The MLA Technology team is looking for candidates who are excited to write software that tests silicon itself—running structural scan (DFT) tests across a huge fleet of machine learning accelerator servers, and turning the results into insights about some of the largest chips ever built.
Do you like the idea of testing billions of transistors and finding the one that’s misbehaving? Great! Ever talked to a chip over JTAG just to see what it would say? Great! Do you read data sheets like others read the news? Great! Written a script to automate something tedious, then kept improving it just because you could? Great! Enjoy turning a mountain of raw data into a dashboard that tells a story? Great! Did this make you smile? Awesome.
Our team writes software that tests the most advanced machine learning chips in the world, at a scale and speed few systems anywhere can match. If that interests you, come join our team!
Key job responsibilities
Own and evolve the software that speeds up structural scan (DFT) test execution across a large fleet of ML accelerator servers
Design and deliver improvements to the tools and workflows used to bring new scan test patterns to production, from architecture through deployment
Build data analytics and dashboards that track test pattern execution across the fleet
Automate silicon debug and large-scale investigation workflows
Partner with the teams that run and validate scan testing at scale, turning their needs into maintainable, tested, reusable software
Mentor early-career engineers and raise the bar through design discussions and thorough, but kind, code reviews
A day in the life
As an SDE II on the team, you’ll own problems end to end—designing the solution, writing the code, testing it against real hardware, and shipping it to a fleet. One day you might be profiling scan execution to shave seconds off a test that runs across thousands of machines; another day you might be building a dashboard that shows how a new test pattern is behaving across the fleet, or automating complex silicon investigations. No software is too low level for us. No software is too high level for us. You’ll work alongside senior engineers deep in silicon test, and help early-career engineers on the team grow through your designs and code reviews.
About the team
We are not a team of machine learning practitioners—we are a software team focused on testing and understanding the silicon that enables accelerated machine learning workloads at scale, including the chips behind AWS Trainium and AWS Inferentia.
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help you develop your engineering expertise so you feel empowered to take on more complex tasks in the future.
Amazon job ID: 10577153
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