Software Development Engineer II, Data Engineering, Annapurna Labs

Amazon is hiring a Software Development Engineer II, Data Engineering, Annapurna Labs in Austin, United States. It pays $144k-$194k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this data engineer role Little AI ●○○○: aI is not part of the work.
Pay on this posting starts in line with the typical data engineer posting on Level.
Level classes it as a mid-level role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Annapurna Labs designs the silicon behind AWS machine learning acceleration. Trainium and Inferentia servers train and serve the largest models our customers run, and before any of that hardware carries customer traffic it has to prove it works. MLA Vetting owns that gate. We build the diagnostic tests that every accelerator server runs before it becomes sellable, and we decide which failures block a host, which route to a technician for repair, and which are noise.

That gate only works if we can see through it. We are hiring a Software Development Engineer II to build the data and analytics systems that tell us what our tests are actually doing across the fleet. You will build the pipelines that ingest diagnostic results from tens of thousands of servers, the dashboards that make test behavior legible, and the metrics and alarms that tell us a test has started failing on a hardware generation before it costs us a week of capacity. When the team decides whether a diagnostic has enough evidence to move from observation into blocking production, your data answers that question.

This is a software engineering role, not a reporting role. You will write and operate production code, own the alarms and metrics that page us, and build detection for trends nobody is watching yet. You will work directly with hardware, firmware, provisioning, and data center operations teams who act on what you build.

You are fluent in Python and SQL and have built production data pipelines at scale. Experience with Amazon Redshift, Apache Spark, workflow orchestration, and dashboarding in Grafana or QuickSight maps directly to what you will work on here. Experience with telemetry or diagnostic data from a large server fleet, or with defining metrics and anomaly detection on operational time-series data, will have you productive faster. Familiarity with server hardware or data center operations is a plus, not a requirement — we will teach you the hardware.

Key job responsibilities
Design, build, and operate production data pipelines that ingest diagnostic, telemetry, and repair-ticket data from the Trainium and Inferentia fleet into a warehouse other teams query with confidence.

Build and own the metrics, alarms, and anomaly detection that surface test regressions, failure-rate shifts, and new failure signatures across hardware generations without a human going looking for them.

Build dashboards and visualizations that make fleet and test health legible to engineers, hardware partners, and leadership, covering failure rates, failure-signature breakdowns, first pass yield, and repair latency.

Analyze large-scale fleet data to find root cause behind failure trends, and separate genuine hardware faults from software defects and test noise — a distinction that decides whether a failure reaches a technician or an engineer.

Define the evidence standard that gates operational decisions, including whether a diagnostic has soaked long enough and cleanly enough in the fleet to move from observation mode into blocking production.

Improve data quality and pipeline reliability so downstream consumers trust the numbers without re-deriving them.

Write clear analyses and design documents for technical and non-technical readers, including leadership.

A day in the life
You start by checking an alarm that fired overnight: a diagnostic's failure rate climbed on one server generation. You query the failure signatures, find the increase concentrates in one component and one data center, and hand the breakdown to the hardware team by mid-morning. The rest of the day goes to a pipeline you are building to join repair outcomes against diagnostic results, so the team can measure how often a repair actually fixes the fault it was dispatched for. You close the day reviewing a teammate's code review and answering an operations partner who needs a new cut of yield data.

About the team
MLA Vetting sits between hardware manufacturing and customer-ready capacity. We own the diagnostic test suites and the triage framework that turn a raw hardware failure into a specific, actionable repair, and we own the fleet-scale evidence that drives hardware and firmware improvements upstream. Our work is measured in how fast a server reaches sellable and how often it gets there on the first try.

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
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience programming with at least one software programming language

Preferred qualifications

- 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

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 - 143,700.00 - 194,400.00 USD annually

How we rate this

Software Development Engineer II, Data Engineering, Annapurna Labs at Amazon rates 21 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● 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

Software Development Engineer II, Data Engineering, Annapurna Labs is an open job at Amazon.
Level rates this role AI Level 1 of 4: Little AI.
The listed salary is $144k-$194k a year.
The role is based in US, TX, Austin.
The job was posted on October 6, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon