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.
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
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
