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DensityAI

High-density AI infrastructure for data centers

Hardware Reliability Engineer

Hardware Validation & TestingConfirmed live in the last 24 hours
Full-timeMountain View, CA, USAIn-person

About the Job

Requirements

  • A Master of Science or Ph.D. in Materials Science, Mechanical Engineering, Electrical Engineering, Applied Physics, or a related field.
  • At least 5 years of experience in 2.5D/3D advanced packaging reliability.
  • Deep command of physics-of-failure methodology and strong materials-science knowledge, particularly interconnects and interfaces.
  • Proficiency in statistical reliability analysis, including Weibull, lognormal, and acceleration modeling, using JMP, Minitab, or Python.
  • Hands-on failure analysis experience with C-SAM, X-ray CT, SEM, TEM, FIB, and EBSD.
  • A proven track record driving OSAT/partner improvements and managing qualifications.
  • Familiarity with JEDEC, IPC, IEEE, and MIL-STD standards.

Responsibilities

  • Conduct physics-of-failure modeling for advanced accelerator packaging; assess thermal, mechanical, and electrical stressors; and define and execute stress-test protocols including thermal cycling, electromigration, HTOL, HAST/uHAST, and power cycling.
  • Lead failure-mode analysis using C-SAM, X-ray CT, SEM, TEM, FIB, and EBSD; identify cracking, voiding, electromigration, and stress-induced damage; and drive corrective and preventive action using 8D and FMEA.
  • Build and apply Coffin-Manson, Arrhenius, and Black's equation models and FEA-based stress simulation to predict field lifetime and FIT under real accelerator thermal and power profiles.
  • Partner with assembly and test providers on reliability improvements; define requirements, ensure JEDEC/IPC/IEEE/MIL-STD compliance, monitor OSAT performance, and support supplier audits and qualifications.
  • Assess thermal, mechanical, and electrical stress interactions across the package, board, and accelerator system; and drive design-for-reliability into the package and board-to-package interface with packaging, materials, signal integrity/power integrity, and thermal teams.
  • Develop design guidelines and reliability best practices, and own the reliability data presented to internal teams and customers.
  • Translate package- and system-level reliability into fleet availability targets, including AFR, FIT, MTBF/MTTR, and availability nines; drive detection and mitigation of silent data corruption or silent data errors in production; close the loop from field telemetry, returns, and RMA into design and qualification through reliability growth; and partner with data-center operations, SRE/hardware operations, and customers on serviceability and uptime for large-scale training and inference.
  • Use and develop AI-assisted and machine-learning tool flows to accelerate failure analysis, lifetime modeling, and failure prediction.

Desired Qualifications

  • Experience with heterogeneous integration, fan-out packaging, chiplet architectures, HBM, or silicon-photonics packaging.
  • Knowledge of electrical reliability mechanisms such as electromigration and dielectric/TDDB breakdown.
  • Experience with design-for-reliability, prognostics, and health management for electronic systems.
  • Experience with AI-driven reliability modeling or machine learning for failure prediction.
  • Experience with high-power/high-current package reliability for accelerators or GPUs, and customer-facing qualification.

About the Company

DensityAI

High-density AI infrastructure for data centers

DensityAI designs high-density, full-stack AI infrastructure for data centers, with custom chips, hardware, and software tightly integrated. Its products are aimed at automotive and robotics applications, delivering end-to-end AI workflows from perception and decision-making to control. The platform leverages hardware-software co-design to achieve higher performance, better power efficiency, and lower costs, addressing scalability and sustainability in AI computing. Unlike typical vendors, DensityAI combines a strong ex-Tesla leadership with a team of Dojo alumni and other industry veterans to offer a plug-and-play solution for OEMs and operators, simplifying the deployment of AI infrastructure. The company plans to raise substantial funding to speed development, validate deployments in its core markets, and broaden into general AI data center use and a comprehensive developer ecosystem.

Company size
51-200
Company stage
Acquired
Total funding
Not listed
Headquarters
Mountain View, CA
Founded
2024

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