Nvidia

Nvidia

Senior Failure Analysis Engineer

US, CA, Santa Clara · Senior

Sponsorship not specifiedDetected 2 days ago
Node.jsFull-Stack DevelopmentDeep LearningSEMExcelRoboticsElectrical EngineeringMicroscopyAdaptability

About the role

  • NVIDIA is the pioneer of GPU-accelerated computing and the engine powering the global AI revolution.
  • Physical Failure Analysis team is relentlessly committed to quality, pushing the boundaries of semiconductor innovation and hardware reliability.
  • Nanoscale Analysis: Apply advanced Focused Ion Beam (FIB) sample preparation and analysis techniques to enable high-resolution electrical and physical diagnostics of IC chips.

Responsibilities

  • Develop Advanced FA Techniques & Methodologies Pioneer Novel Methods: Design and implement cutting-edge methodologies to drive successful root-cause investigations on leading-edge silicon, advanced heterogeneous packaging technologies, and complex integrated systems.
  • Precision De-processing: Perform mechanical cross-sectioning, parallel de-layering, and advanced chemical/plasma de-processing of integrated circuit (IC) devices across die, package, component, and board levels.
  • Innovate Characterization Platforms: Develop novel characterization techniques and specialized analysis platforms tailored for emerging chip structures with advanced silicon architectures and complex packaging schemes.
  • Optimize Workflows: Design and implement robust failure analysis (FA) workflows to improve laboratory efficiency and guarantee accurate, reproducible root-cause determination.
  • Conduct In-Depth FA Investigations Lead Root-Cause Investigations: Head high-impact failure analysis investigations to isolate the root causes of critical failures affecting product yield, performance, and long-term reliability.
  • Support multi-functional Engineering: Deliver precise physical characterization data to support engineering teams with Design of Experiments (DOE) evaluations, design optimization, process node improvements, and critical customer FA requests.
  • We build the full-stack AI infrastructure, advanced microelectronics, and hardware ecosystems that drive everything from massive data center AI factories to autonomous vehicles, robotics, and advanced scientific simulation.
  • In this role, you won't just solve problems-you will work at the dynamic intersection of physics, process technology, physical design, and circuit design to investigate complex product failures and drive critical engineering solutions.

Requirements

  • Proven proficiency in precision IC device sample preparation for targeted defect localization and physical root-cause analysis.
  • Strong working knowledge of physical and electrical failure analysis across die, package, component, and board levels.
  • Sample Preparation: Proven proficiency in precision IC device sample preparation for targeted defect localization and physical root-cause analysis.

Compensation

  • Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
  • The base salary range is 144,000 USD - 230,000 USD.

Benefits

  • With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers.
  • You will also be eligible for equity and benefits.
  • Education & Experience: A B.S., M.S., or Ph.D. in Electrical Engineering, Materials Science, Physics, or closely related field (or equivalent experience), paired with 8+ years of relevant, hands-on industry experience.

Company info

  • We are seeking a highly skilled and motivated Failure Analysis Engineer to join our world-class Failure Analysis Lab.
  • We are looking for an adaptable technical expert capable of translating open-ended challenges into actionable insights.

Equal opportunity

  • NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.
  • equal opportunity employer.

This listing is sourced directly from Nvidia's careers page and normalized into a canonical job model.