Six Figure Work

DFT Design Engineer, Machine Learning Acceleration

Amazon

Company websiteamazon.com (Amazon, opens in new tab)

Base salary $153,600  to $215,000

Employer-published annual base salary, USD. Advertised base range in Austin, TX

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  • LocationAustin, TX
  • WorkplaceWorkplace not stated
  • Status at last checkSource accepting applications when checked
  • Last checked (47 min ago) · observed since

About this job

Custom SoCs (System on Chip) are at the heart of AWS Machine Learning servers. As a member of the Cloud-Scale Machine Learning Acceleration team, you'll be responsible for designing and optimizing hardware in our data centers, including AWS Inferentia and Trainium systems—our custom-designed machine learning inference and training servers. Our success depends on world-class server infrastructure as we handle massive scale and rapidly integrate emerging technologies. We're looking for an ASIC DFT Design Engineer to help us trailblaze new technologies and architectures while ensuring high design quality and making the right trade-offs.

Key job responsibilities

  • Define and develop state-of-the-art Design for Test (DFT) architectures for advanced technology nodes
  • Work closely with block designers and physical design (PD) team to implement highly efficient DFT solutions
  • Perform RTL coding and Verification using Verilog/System Verilog
  • Utilize industry standard DFT tools to create high coverage and cost-effective test patterns to target advanced silicon defects
  • Participate in Silicon debug efforts alongside ATE and System teams
  • Communicate and work with team members across multiple disciplines

Basic Qualifications

  • Bachelor's degree in Electrical or Communications Engineering or a related field
  • 5+ years of practical DFT experience with complex SoC designs in advanced technology nodes
  • Experience with standard tools and practices in DFT, including ATPG, JTAG, MBIST and trade-offs between test quality and test time
  • Experience with automation script development

Preferred Qualifications

  • Master's degree in Electrical or Communications Engineering or a related field
  • Practical experience developing STA constraints for DFT modes and working directly with PD teams to close timing
  • Experience with RTL coding and design verification (DV) flows
  • Experience with gate-level simulation setup and debug with SDF
  • Strong programming and scripting skills in Perl, Python or Tcl
  • Practical experience with silicon debug and yield optimization

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 - 153,600.00 - 215,000.00 USD annually

Source: Amazon · View original posting (opens in new tab)

Pay in context

This role's base floor ($153,600) is above 60% of the 69 Data & ML roles in Texas listed on this site; 1 other lists the same floor. Their median floor is $143,640; the middle half runs from $130,000 to $160,000.

Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 6, 2026.

This range next to 6 other live Amazon roles with the nearest advertised base floor
Chart as a table
This range next to 6 other live Amazon roles with the nearest advertised base floor
RolePublished base
DFT Design Engineer, Machine Learning Acceleration (this listing)$153,600 - $215,000
Senior Solutions Architect, National Security Solutions Architecture Team$153,600 - $207,800
ERP Solution Architect, Amazon Robotics Business Applications and Solutions Engineering$153,600 - $207,800
SAP Supply Chain Architect, AWS SAP Team$153,600 - $207,800
SDE, MLA hardware/software co-design, Annapurna Labs Machine Learning Acceleration$153,600 - $215,000
Sr Solutions Architect, BDT$153,600 - $207,800
Sr Solution Architect, Amazon Fuse$153,600 - $207,800

Same $100K to $400K scale as every card. All Amazon roles · How to read the bar

Observed changes

  1. Began meeting the published-salary criteria
  2. First observed on the employer's board
Observed since
Employer-stated posting date
2026-09-01
Where this pay comes from

Location as the employer wrote it: US, TX, Austin

USA, TX, Austin - 153,600.00 - 215,000.00 USD annually

Method
Employer text, read with fixed rules and re-verified
Checked
Rule set
us-base-v2

The pay passage appears in the employer's own job text. Fixed, deterministic rules located it, and the passage below was checked against the stored copy of the posting.

What we verified

  • The pay figures come from the employer's own listing, quoted exactly above.
  • The pay is an annual base salary in US dollars with a minimum of at least $100,000.
  • The role was publicly listed on the employer's job board when last checked.

We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.