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Description

AWS-Annapurna team develops the silicon used in our most advanced machine learning accelerator servers at cutting edge process nodes. These SOCs are used in massively scaled server clusters to provide best hardware platform for our customers to run training and inference workloads.

We are seeking an experienced silicon yield data analysis engineer with expertise in silicon test data analysis, automation and yield debug. This experienced engineer will be responsible for building our data systems which parse data from various ATE and system level test platforms and generating analysis which provide actionable information impacting key product metrics like yield, performance and test cost. They will be responsible for developing analysis dashboards that are widely used across the organization and implementing early warning alert systems to warn the test owners about manufacturing excursions. They will interact with ATE, Systems test teams and Silicon design teams to identify systematic manufacturing issues and work with other product engineers to debug and root cause. This role involves collaborating with various teams to develop innovative solutions to optimize yield and performance for our products. Strong analytical and problem solving skills, knowledge of semiconductor manufacturing process and expertise in statistical analysis are essential for success in this role.

Our final product is a server, not just the silicon, so you will find yourself stretching beyond traditional silicon product engineering boundaries and dealing with various system issues and data sets, providing ample opportunities to learn.

About the team

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.

Basic Qualifications

• Bachelors or Masters in Electrical or Computer engineering

• 5+ years of experience working on semiconductor test data analysis and automation

• 3+ years of experience conducting data analysis of foundry WAT data, ATE test data and/or system level test data using tools like JMP, Python etc.

Preferred Qualifications

• Experience working on yield and power/performance characterization datasets of digital semiconductor chips.

• Basic understanding of fab process flow for leading technology nodes and ability to drive corrective actions based on ATE test data, WAT data and system test data analysis

• Experience building dashboards and automated analysis scripts

• Experience building data analytics systems with AWS tools like S3, Sagemaker and Quicksight

• Proficiency in test data analysis and statistics using tools like JMP and Python.

• Basic understanding of ATE test content to drive debug for SCAN ATPG and SRAM yield issues.

• Experience driving corrective actions across cross-functional teams to fix and root-cause systematic yield issues and improve product yield and cost.

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 https://www.amazon.jobs/en/disability/us.