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M
Mindlance
Failure Analysis & Validation Engineer
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Job Description
Failure Analysis & Validation Engineer#26-27297
Austin, TX
Onsite Job Description
TITLE:
Failure Analysis & Validation Engineer
LOCATION
Austin, TX (ONSITE)
KEY RESPONSIBILITIES
- Develop and execute DOEs to Validate silicon-, board-, and system-level failures.
- Bring up server platforms and test stations to support Failure Analysis for NPI and production.
- Debug hardware, firmware, driver, power, thermal, PCIe and memory Fails.
- Analyze logs, telemetry, and test data to identify failure signatures and dependencies.
- Develop and validate diagnostics, stress tests, and functional tests.
- Interface with multiple cross functional team members to Reproduce, Isolate and Root cause Failures.
- Develop automation for FA workflows.
- Configure and maintain Windows- and Linux-based server test environments.
- Document processes, technical findings, and deliver clear FA reports and reviews.
- Optimize lab workflows and test capability to reduce turnaround time and system downtime.
- Analyze large datasets, develop dashboards, and generate technical reports.
- Travel to Contract Manufacturing Sites to assist with Bring up and Training.
PREFERRED EXPERIENCE
- Strong EE fundamentals, knowledgeable in GPU/CPU architecture, post-silicon validation, platform debug, or test development.
- 5 years of experience in Test, Debug or Validation of semiconductor assemblies.
- Strong knowledge of PCIe, memory, power management and thermal-control subsystems.
- Experience debugging firmware, drivers, operating systems, and hardware interactions.
- Experience with firmware tuning, PVT characterization, voltage, temperature, and frequency margining.
- Programming and automation experience in Python, shell scripting, C++, C#, or equivalent languages.
- Proficiency with oscilloscopes, logic analyzers, multimeters, regulated power supplies, and thermal cameras.
- Ability to interpret schematics, component datasheets, logs, telemetry, and validation data.
- Experience with GPU data-center platforms and air- or liquid-cooled infrastructure is preferred.
- Experience working with AI tools or agent-based automation to engineering workflows is preferred.
ACADEMIC CREDENTIALS
Bachelor's or master's degree in electrical engineering, Computer Engineering, or related field