Verification Methodology Engr, AWS Machine Learning Acceleration

Annapurna Labs
 Cupertino, CA


Job summary

Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. We have data center locations in the U.S., Europe, Singapore, and Japan, and customers across all industries.

As a member of the Cloud-Scale Machine Learning Acceleration team you’ll be responsible for the design and optimization of hardware in our data centers including AWS Inferentia, our custom designed machine learning inference datacenter server. Our success depends on our world-class server infrastructure; we’re handling massive scale and rapid integration of emergent technologies.

“Methodology” refers to the way in which we accomplish our goals - and in the realm of verification, there are many methodologies and tradeoffs to consider. In this role, you’ll be responsible for developing tools, flows, and methodology for verification testbenches in the Machine Learning Acceleration (MLA) product family - in a manner that is optimized, efficient, and quickly deployed. Additionally, you'll be responsible for verification of design units and subsystems of a larger SOC.

Basic Qualifications

  • Experience developing and deploying UVM components, such as bus agents, scoreboards, and register models
  • Experience understanding SoC project milestones, setting goals & requirements and influencing a team of engineers to close on the requirements
  • Experience architecting & deploying flows for running regressions and collecting coverage data
  • Exposure to VLSI design concepts, logic design
  • Familiarity with verifying complex ASICs using random stimulus and functional coverage
  • Ability to write and drive a verification plan based on an architectural specification
  • Experience with best-in-class revision control systems (Git, SVN, Perforce)
  • Experience writing testbenches in SystemVerilog

Preferred Qualifications

  • Hands-on experience with closing code and functional coverage on a design block
  • Experience with advanced git configuration and building git hooks
  • Experience with code review tools such as Gerrit
  • Familiarity with assertion-based verification methodologies
  • Experience creating reusable objects in Python to automate code generation
  • Experience with building within the Portable Test and Stimulus Standard (PSS)

Join us in creating the most advanced Machine Learning Accelerators in the world!

About Us

Inclusive Team Culture

Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance

Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

Key job responsibilities

  • Develop verification methodologies to be used across the entire verification team
  • Invent new solutions to eliminate identified inefficiencies
  • Maintain and enhance base classes within UVM framework
  • Participate in developing methodologies for SOC design
  • Display strong debug skills, with the ability to learn and resolve complex, multi-layered issues
  • Guide tool and licensing requirements based on Verification team needs
  • Partner with 3rd party EDA vendors to implement new technologies in our workflows
  • Provide documentation, training, and support for developed methodologies

A day in the life

  • Move at a fast pace, make the best technical decisions for the team
  • Automate running of tests, regressions and scale the solutions
  • Be a leader, work with stakeholders to make appropriate trade-offs, be willing to take the right risks
  • Constantly raise the bar for innovative and efficient solutions
  • Work with a strong SoC, SW and architecture team to make impactful changes to the product
  • Develop, run and debug tests at the SoC level

About the team

The Amazon Annapurna Labs team is responsible for building innovation in silicon and software for AWS customers. Annapurna is at the forefront of innovation by combining cloud scale with the world’s most talented engineers. Our team covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. Our team is focused on delivering the best in-class AWS cloud infrastructure solutions in machine learning with AWS Neuron, Inferentia and Trainium ML Accelerators.

Basic Qualifications

  • B.S. Computer Engineering or related technical field
  • 5+ years of experience with C/C++ and/or SystemVerilog/UVM
  • 5+ years of experience developing scalable tests for emulation platforms OR 5+ years of experience doing design verification + interest in moving to emulation platform running HW/SW workloads
  • Experience integrating internal and external IPs (design and verification)
  • Experience with emulation infrastructure (xtors, emulator capacity, ViPs, assertion enablement)
  • Experience defining and driving HW/SW performance goals

Preffered Qualifications

  • M.S. or Ph.D. in Computer Engineering or related field
  • Experience with RTL programming
  • Experience analyzing data created by emulation platform
  • Experience with automation of repetitive processes

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit