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职位Google

Failure Analysis Engineer, Hardware Reliability

Google

Failure Analysis Engineer, Hardware Reliability

Google

·

On-site

·

Full-time

·

2w ago

About the job

The Failure Analysis and Materials team is responsible for investigating failures across all hardware products made by Google. On this dynamic team, you will work with a team of engineers of all types (mechanical, electrical, optical, materials science, chemical) to solve multi-disciplinary problems relating to hardware failures during product development and from the field. We develop test methods, collaborate with cross-functional partners, and support products from early prototypes through field failures.

Google's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.

Responsibilities

  • Contribute to the investigation of hardware failures in systems, PCBs, discrete components and silicon ICs, including but not limited to: electrical, material, and mechanical failures in design, process, and system integration.

  • Identify failure modes in hardware, by forming failure hypothesis and validation plans with system/circuit analysis, and carrying out the plan experimentally utilizing techniques such as optical microscopy, CT, and electrical probing.

  • Develop clear failure analysis documentation that can be read by both technical and non-technical audiences.

  • Develop and optimize new analytical methods and laboratory procedures to address emerging challenges and improve failure analysis capabilities.

  • Work closely with cross-functional teams to provide actionable recommendations and support corrective actions.

Minimum qualifications

  • Bachelor’s in Mechanical/Chemical/Electrical Engineering, Materials Science, or equivalent practical experience with a focus on failure analysis.

  • 4 years of failure analysis experience in the field of consumer electronics, semiconductor devices and package, medical devices, aerospace/defense, solar, or automotive.

  • Experience with running and utilizing lab tools (microscope, EE probing tools).

Preferred qualifications

  • Master's degree in Electrical Engineering, Material Science or equivalent experience with a focus on experimental work.

  • Experience with inspection equipment (X-ray, CT), FIB and SEM.

  • Experience in experimental automation, instrument control, data analysis tools, numerical simulations, or statistical analyses.

  • Experience programming in one or more programming languages or GenAI tools: MATLAB, Python, R, C/C++, JavaScript.

  • Ability to travel up to 25% of the time, including international travel.

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关于Google

Google

Google

Public

Google specializes in internet-related services and products, including search, advertising, and software.

10,001+

员工数

Mountain View

总部位置

$1,700B

企业估值

评价

3.7

25条评价

工作生活平衡

3.8

薪酬

4.2

企业文化

3.4

职业发展

3.9

管理层

2.8

68%

推荐给朋友

优点

Excellent compensation and benefits

Smart and talented colleagues

Great perks and work flexibility

缺点

Management and leadership issues

Bureaucracy and slow processes

Constantly changing priorities and reorganizations

薪资范围

57,502个数据点

Junior/L3

L3

L4

L5

L6

L7

L8

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

Junior/L3 · Data Scientist L3

0份报告

$176,704

年薪总额

基本工资

-

股票

-

奖金

-

$150,298

$203,110

面试经验

9次面试

难度

3.4

/ 5

时长

14-28周

录用率

44%

体验

正面 0%

中性 56%

负面 44%

面试流程

1

Application Review

2

Online Assessment/Technical Screen

3

Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Product Sense