Product Development Engineer, Annapurna Labs Silicon Operations

Amazon Amazon · Big Tech · Austin, TX · Software Development

This role focuses on silicon yield product engineering for AWS-Annapurna Labs, optimizing manufacturing processes for machine learning accelerator chips. Responsibilities include analyzing test data, developing automation systems, leading test coverage improvements, and creating dashboards for yield and quality monitoring. The role requires expertise in semiconductor manufacturing, statistical analysis, and problem-solving, with a focus on improving yield and performance of ML chips used in training and inference workloads.

What you'd actually do

  1. Perform detailed data analysis of ATE Test, SLT test, and System test data to optimize yield, test time, and implement optimal screening methodologies across platforms.
  2. Develop and maintain automation systems to compare yields across OSATs, testers, and setups, while monitoring performance metrics to drive timely corrective actions.
  3. Lead functional and structural test coverage improvements at ATE and system levels through strategic DOE planning and targeted characterization efforts to collect critical performance data.
  4. Design and maintain comprehensive dashboards enabling cross-functional teams to monitor key metrics, with automated alert systems for rapid issue identification and resolution.
  5. Foster strong stakeholder relationships and drive corrective actions for yield and quality improvements through effective collaboration with test engineering, system validation, and DFT teams

Skills

Required

  • Bachelor's degree in Electrical Engineering or a 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

Nice to have

  • Experience with yield and performance optimization at system or ATE test on advanced FINFET nodes
  • Understanding of ATE test content (Scan, BIST, Functional, IO tests) and experience setting test limits based on characterization.
  • Experience with power, performance characterization on high performance chips.
  • Knowledge of usage of ATPG scan diagnostics and SRAM bitmap analysis for FA and yield debug.
  • Proficiency in statistical analysis tools (JMP, Python) and automation for semiconductor test data.
  • Experience building automated analysis systems and interactive dashboards for yield and quality monitoring.
  • Familiarity with AWS services (Sagemaker, S3, Quicksight etc.) and ability to use these for automation.

What the JD emphasized

  • machine learning chips
  • machine learning accelerator servers
  • training and inference workloads