Silicon Yield Engineer - ML Accelerator Chips

Amazon; Austin (TX); On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Job summary

Amazon, through its AWS Annapurna team, seeks a Silicon Yield Product Engineer in Austin, TX to optimize yield for next-generation machine learning silicon. You will work with ATE, systems test, and silicon design teams to identify root causes and implement robust screening and monitoring solutions.

The role emphasizes data-driven yield improvements, development of automated dashboards, and close collaboration with test engineering, validation, and DFT teams.

Qualifications

• Bachelor's degree in Electrical Engineering or related field.
• 2+ years of semiconductor industry experience as a product/test engineer analyzing test data.
• Experience using Python or other scripting languages for data analysis and automation.
• Strong analytical and problem-solving skills.

Responsibilities

• Perform detailed data analysis of ATE Test, SLT test, and System test data to optimize yield and test time.
• Develop and maintain automation systems to compare yields across OSATs, testers, and setups.
• Lead functional and structural test coverage improvements at ATE and system levels through DOE planning and characterization.
• Design dashboards enabling cross-functional teams to monitor key metrics with automated alerts.
• Foster stakeholder relationships and drive corrective actions for yield and quality.

Skills

Python scripting

Data analysis

Electrical engineering background

Analytical thinking

Education

Bachelor's degree in Electrical Engineering

Tools

JMP

ATPG scan diagnostics

SRAM bitmap analysis

Job description

Amazon, through its AWS Annapurna team, seeks a Silicon Yield Product Engineer in Austin, TX to optimize yield for next-generation machine learning silicon. You will work with ATE, systems test, and silicon design teams to identify root causes and implement robust screening and monitoring solutions.

The role emphasizes data-driven yield improvements, development of automated dashboards, and close collaboration with test engineering, validation, and DFT teams.