AI Hardware Systems Engineer, Annapurna Labs, Trainium Machine Learning Fleet Operations

Amazon is hiring an AI Hardware Systems Engineer, Annapurna Labs, Trainium Machine Learning Fleet Operations in Austin, United States. It pays $136k-$184k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
Tools and skills the posting cites include Python, Bash and hardware debugging.
Level classifies it as a senior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

AI Hardware Systems Engineer managing fleet operations, debugging, and automation for machine learning server hardware.

PythonBash

hardware-debuggingfleet-operationsautomationmachine-learning-infrastructuresystem-testing

Annapurna Labs 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 Machine Learning Acceleration Fleet Operations Team is looking for candidates interested in diving deep into our fleet of ML servers deployed around the world.

We are seeking an engineer who is comfortable debugging emergent problems in GPU and server hardware, writing scripts in languages such as Python or Bash, running large scale experiments on a fleet of complex hardware, developing data infrastructure and analyzing trends, and developing automation software to scale operations.

Our team has end to end ownership of some of the most advanced server hardware in the world. We drive technical debug efforts and write truly massive scale autonomous software to monitor, optimize, and remediate machine learning hardware. Come join us!

Key job responsibilities
- Member of a team responsible for system remediation, operational excellence, and customer experience on bleeding edge ML products
- Utilize data to root cause hardware failures and identify live trends on the most complex systems in AWS
- Implement and improve system level testing across the product lifecycle
- Develop software which can be maintained, improved upon, documented, tested, and reused
- Dive deep on issues at the intersection of hardware and software

A day in the life
As a Platform Development Engineer, you are the dedicated owner of an ML server platform in our fleet. Your mission is to maximize its health, sellability, and customer experience.

You start each day with eyes on the fleet — reviewing dashboards to identify trends and triaging emergent issues, then partnering with hardware and software engineering teams to debug, investigate, and translate findings into permanent fixes. You own the end-to-end testing story and manage tradeoffs between coverage and velocity. You direct new automations, tooling, and data infrastructure to scale your operations. You manage software deployments, debug issues with them, and run status meetings to align all platform stakeholders on how the product is performing.

About the team
The MLA Fleet Operations team was formed to maintain an exceptionally high quality bar for our fleet of advanced machine learning accelerators and server products. We perfect the customer experience by developing scalable software for rapid incident response times and data visualization as well as diving deep into hardware issues as they arise.

Basic qualifications

- 2+ years of non-internship professional software development experience
- 1+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience
- 1+ years of administrative experience in networking, storage systems, operating systems and hands-on systems engineering experience
- Knowledge of systems engineering fundamentals (networking, storage, operating systems)
- Experience programming with at least one modern language such as C++, C#, Java, Python, Golang, PowerShell, Ruby
- Experience with Linux/Unix
- Experience debugging and systems analysis to identify and quickly resolve or mitigate issues
- Bachelor's degree in Computer Science, Computer Engineering, or Electrical Engineering

Preferred qualifications

- 4+ years of hardware design and validation of components, subsystems and systems experience
- Experience with post-silicon validation
- Master's degree in Computer Science, Computer Engineering, or Electrical Engineering

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 - 136,000.00 - 184,000.00 USD annually

How we rate this

AI Hardware Systems Engineer, Annapurna Labs, Trainium Machine Learning Fleet Operations at Amazon rates 60 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● 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

AI Hardware Systems Engineer, Annapurna Labs, Trainium Machine Learning Fleet Operations is an open job at Amazon.
Level rates this role AI Level 3 of 4: Works on AI.
The listed salary is $136k-$184k a year.
The role is based in US, TX, Austin.
The job was posted on September 9, 2026.
Seniority: senior.
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

Hardware DebuggingFleet OperationsAutomationMachine Learning InfrastructureSystem TestingPythonBash

Questions you could be asked

Tell me about a project where hardware debugging was part of your work. What did you do?
Tell me about a project where fleet operations was part of your work. What did you do?
Tell me about a project where automation was part of your work. What did you do?
Tell me about a project where machine learning infrastructure was part of your work. What did you do?
Tell me about a project where system testing was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Hardware Debugging, Fleet Operations, Automation, Machine Learning Infrastructure, and System Testing. 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.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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