Software Development Engineer, Machine Learning Accelerator Technology

- 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 programming with at least one software programming language
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Experience in embedded development in C/C++
- Knowledge of computer architecture, operating systems, and parallel computing
- Experience writing low level drivers
- Experience in debugging, profiling, and implementing software engineering best practices in large-scale systems

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USA, TX, Austin - 143,700.00 - 194,400.00 USD annually
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.