AI ML Intern Jobs in Austin, TX
AI ML Intern jobs in Austin, Texas concentrate in the Domain, East Austin, and the downtown tech corridor, where demand across enterprise software, semiconductor design, and healthtech is strong year-round. Employers posting roles right now include Amazon Web Services, AMD, and Amazon. Scan the live roles below and apply to whichever ones fit.
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DESCRIPTION
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
DESCRIPTION
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
See All 32 AI ML Intern Jobs in Austin
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Find AI ML Intern JobsAI ML Intern Job Market in Austin
Who's Hiring
- Amazon Web Services16
- AMD6
- Amazon6
- Meridial3
Top Industries Hiring
- Technology & Software10
- Retail6
- E-Commerce & Online Marketplaces6
AI ML Intern Jobs in Austin: Frequently Asked Questions
How do I get a ai ml intern job in Austin?
Target Austin's highest-concentration hiring sectors: enterprise software companies clustered in the Domain, semiconductor and hardware firms in North Austin, and healthtech startups in East Austin. Candidates who stand out bring hands-on Python or model-training project work, even from coursework or personal repositories. UT Austin and ACC connections also give local candidates an edge, since many Austin employers recruit directly from those pipelines.
Which companies hire ai ml interns in Austin?
Austin ai ml intern roles are posted by Amazon Web Services, AMD, and Amazon and others right now, based on current listings on Migrate Mate as of November 2026. Austin's hiring mix skews toward high-growth tech firms, established semiconductor companies, and a dense layer of venture-backed AI startups that treat internships as a direct recruiting pipeline.
Are there remote ai ml intern jobs in Austin?
Yes, though availability depends heavily on the role type. Model training, data labeling, and research-oriented tasks tend to be remote-compatible, while hardware integration or lab-based ML work typically requires in-person presence. About 100% of ai ml intern openings tied to Austin are remote or hybrid as of November 2026, and the analytical and software-heavy roles at Austin's enterprise tech employers are the most likely to offer that flexibility.
How can I get a ai ml intern job in Austin with little or no experience?
The most realistic entry path in Austin is through project-based applications: build a small, documented ML project on GitHub and lead with it. Austin employers, especially early-stage AI startups in East Austin and research arms of larger tech firms, regularly hire interns who show applied curiosity over formal experience. Research assistant roles at UT Austin's AI labs are also a strong lateral entry point that local hiring managers recognize.
Which industries hire the most ai ml interns in Austin?
Most ai ml intern openings in Austin sit in Technology & Software, Retail, and E-Commerce & Online Marketplaces, per current listings on Migrate Mate as of November 2026. Austin's combination of a major research university, a dense semiconductor manufacturing base, and one of the fastest-growing enterprise software ecosystems in the country makes those sectors the consistent drivers of local ML intern demand.
See All 32 AI ML Intern Jobs in Austin
Find roles in Austin that match your experience and apply in just a few clicks.
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