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DensityAI

High-density AI infrastructure for data centers

Hardware Reliability Engineer

Full-Time
$132k - $220k+ Equity grant
Senior
Master's, PhD
Mountain View, CA, USA
In Person
Hardware EngineeringHardware Validation & Testing
H1B Sponsorship Available

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.
  • Command of physics-of-failure methodology and materials-science knowledge, particularly interconnects and interfaces.
  • Proficiency in statistical reliability analysis, including Weibull and lognormal distributions, acceleration modeling, and JMP, Minitab, or Python.
  • Hands-on failure analysis experience with C-SAM, X-ray CT, SEM, TEM, FIB, and EBSD.
  • A track record of driving OSAT and 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 shipped system; and drive design-for-reliability at the package and board-to-package interface with packaging, materials, SI/PI, and thermal teams.
  • Develop design guidelines and reliability best practices, and own 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 and silent data errors in production; incorporate field telemetry, returns, and RMA into design and qualification through reliability growth; and partner with data-center operations, SRE/hardware-ops, 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, including 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 or high-current package reliability for accelerators or GPUs, and customer-facing qualification.

About the company

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

N/A

Headquarters

Mountain View, California

Founded

2024

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Simplify Jobs

Simplify's Take

What believers are saying

  • Andreessen Horowitz is reportedly leading hundreds of millions at a $10 billion valuation.
  • AWS signed a conditional purchase agreement for chips meeting performance targets.
  • Tesla senior hardware executive Shishuang Sun joined DensityAI in July 2026.

What critics are saying

  • DensityAI still lacks a shipped chip; the AWS contract stays conditional on benchmarks.
  • 3D DRAM packaging faces heat and yield problems that kill schedules.
  • A failed round or benchmark miss destroys the $10 billion valuation overnight.

What makes DensityAI unique

  • Ganesh Venkataramanan, Bill Chang, and Ben Floering bring Tesla Dojo system experience.
  • DensityAI combines custom chips, hardware, and software for AI data centers.
  • Its 3D DRAM stacking bet targets bandwidth, capacity, and energy efficiency.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • Paid Vacation
  • Company Equity

Growth & Insights and Company News

Headcount

6 month growth

82%

1 year growth

58%

2 year growth

58%
Data Center Dynamics
Sep 25th, 2026
Ex-Tesla Dojo team's AI chip startup DensityAI valued at $10B after funding round

AI chip startup DensityAI has raised hundreds of millions of dollars in a funding round valuing the company at $10 billion, according to The Information. Andreessen Horowitz is reportedly in talks to lead the round. The company was founded in 2025 by former Tesla Dojo chip program leaders Ganesh Venkataramanan, Bill Chang, and Ben Floering. Around 20 employees left Tesla for DensityAI after Elon Musk dissolved the Dojo team in August 2025. DensityAI plans to develop data centre chips using 3D DRAM stacking to improve speed and energy efficiency. The report suggests the startup has secured a conditional purchase agreement with AWS, though Amazon declined to comment. Previous investors include Dolby Family Ventures, South Park Commons, and Firestreak Ventures.

Crypto Briefing
Sep 25th, 2026
DensityAI nears $10B valuation with hundreds of millions in funding and AWS deal

DensityAI nears $10B valuation with hundreds of millions in funding and AWS deal. The Tesla Dojo spinoff lands Andreessen Horowitz backing and a conditional chip purchase agreement with Amazon Web Services 5 hours ago Sponsored: Vera - AI-powered prediction market intelligence, built for serious analysts Explore Vera A startup that barely existed 18 months ago is now worth as much as some publicly traded semiconductor companies. DensityAI, the AI chip venture founded by former Tesla Dojo engineers, is closing a funding round that values the company at roughly $10 billion, with Andreessen Horowitz leading the deal. The round is expected to bring in hundreds of millions of dollars in fresh capital. More importantly, DensityAI has locked in a conditional procurement agreement with Amazon Web Services, meaning AWS will buy DensityAI's chips if they hit certain performance benchmarks. From Tesla's basement project to a $10B valuation. DensityAI was founded in 2025 by Ganesh Venkataramanan, who previously led Tesla's Dojo supercomputer initiative. He brought roughly 20 engineers from the Dojo team with him. The company's trajectory since then has been aggressive. DensityAI closed a seed round in September 2024, followed by an early-stage venture round in September 2025. Now, roughly a year later, it's commanding a valuation that puts it in the upper echelon of private AI companies globally. AI, tech, and the markets they move - in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. The AWS agreement and what it actually means. The conditional purchase agreement with AWS is the centerpiece of DensityAI's pitch to investors. Under the deal, AWS commits to buying DensityAI's custom AI chips, but only if those chips meet predefined performance standards. AWS has been diversifying its chip supply chain for years, developing its own Graviton and Trainium processors while also maintaining its relationship with Nvidia. Adding DensityAI to the mix would give Amazon another option for specialized AI workloads, reducing dependence on any single supplier. A crowded market with very high stakes. What separates DensityAI, at least on paper, is its focus on building integrated solutions spanning chips, hardware, and software for data centers. The company targets sectors including automotive, robotics, and broader AI applications, areas where the Dojo team's experience with Tesla's compute-intensive workloads could provide a genuine edge. Disclosure: This article was edited by Diego Almada Lopez. For more information on how Crypto Briefing create and review content, see its Editorial Policy.

Xiamen Precise Display
Aug 25th, 2026
Tesla senior chip engineer joins DensityAI as in-house plans face more strain.

Tesla senior chip engineer joins DensityAI as in-house plans face more strain. * August 25, 2026 * Posted by jeff Tesla's senior director of AI hardware design, Shishuang Sun, joined DensityAI in July 2026, taking charge of packaging and system hardware development. The move came as Tesla continued to pursue custom AI and robotics chips amid the loss of another senior hardware leader.

Electrek
Jul 24th, 2026
Tesla closes secret $2B AI hardware deal, calls deployment milestones 'improbable

Tesla has closed its mysterious acquisition of an AI hardware company for $1.95 billion in stock and equity awards, according to the company's Q2 2026 10-Q filing. The deal was first disclosed in April but Tesla has still not named the target company. Of the total amount, only $222 million was allocated to actual assets — a patent and related technology. The remaining $1.73 billion is contingent on service conditions and performance milestones tied to successful technology deployment. Notably, Tesla recorded no stock-based compensation expense in Q2, stating the performance conditions were "improbable" to be achieved. This assessment raises questions about a deal Tesla valued at nearly $2 billion. The acquisition fits Tesla's broader 2026 investment strategy focused on AI compute and chip development. However, the lack of transparency around the deal has drawn criticism, as shareholders face dilution from hundreds of millions of newly issued shares without knowing the acquisition target or its strategic rationale.