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Application Engineer

Reposted Yesterday
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Remote
Hiring Remotely in IND
Mid level
Remote
Hiring Remotely in IND
Mid level
Lead development of iJTAG (IEEE 1687) based test solutions and DFT methodologies (scan, BIST, hierarchical access). Build AI/ML features for pattern compaction, failure prediction, and root-cause analysis. Develop scalable automation frameworks in Python/C++ and streamline workflows using Tcl/Perl/Shell. Create APIs and pipelines for large-scale ingestion of ATE logs and STIL/WGL patterns, and produce data processing and visualization dashboards.
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Job Summary & Responsibilities
  • Lead development of iJTAG (IEEE 1687) based test solutions, including scan network modeling, instrument access, and test pattern execution.
    • Design and implement DFT methodologies including scan, BIST, and hierarchical access mechanisms.
    • Build AI/ML features in DFT tools for pattern compaction, failure prediction, and scan-failure root-cause analysis
    • Develop scalable automation frameworks in Python/C++ and streamline DFT workflows using Tcl/Perl/Shell
    • Build APIs and pipelines for Large-scale data ingestion (ATE logs, STIL/WGL patterns) and data processing and visualization dashboards
Preferred Qualifications

BE/ME Computer Science, Electronics Engineering or related professions with at least 2-5 years working experience in software development
• Understanding of Basic DFT concepts (scan chains, testability basics) and semiconductor testing fundamentals
• Strong programming expertise in C,C++ or Python and experience in data structures, algorithms, and scalable system design in Linux OS
• Strong attention to details, verbal communication and written skill sets, and ability to work with a highly diverse team-structured environment with positive attitude is mandatory
• Ability to work in a fast paced, project oriented, team environment and escalate any critical issues to the senior management.

Desired
• Exposure to ATE platforms (V93000 or similar) Linux-based development
• Basic knowledge of iJTAG / IEEE 1687 concepts
• Contributions to test optimization/yield improvement frameworks
• Interest or exposure to AI/ML data analysis, automation, or simple ML models applied to test/debug

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