Hardware Qualification Engineer, Machine Learning
Job Summary
Job Description
- Bachelor's degree in relevant field
- Experience with testing requirements and algorithms
- Experience with lab equipment (scopes, analyzers)
- Scripting and automation for test execution and analysis
- Analyze results and ensure they meet Google's requirements
- Report bugs and motivate corrective action
- Master's or PhD degree
- Experience with signal integrity and power electronics
- Experience with PCI Express protocol
- Knowledge of BERTs
3 skills required
Job Details
Minimum qualifications:
- Bachelor’s degree in Electrical Engineering, Computer Engineering, a related field, or equivalent practical experience.
- Experience with testing requirements, best practices, and algorithms for hyperscale hardware or systems, and using scripting and automation to execute test algorithms and analyze results.
- Experience with the use of lab equipment (e.g., scopes, analyzers, oscilloscopes, etc).
Preferred qualifications:
- Master's or PhD degree in Electrical Engineering, Computer Engineering, or a related field.
- Experience working with signal integrity in a data center or hyperscale technical environment, particularly focused on power electronics and power delivery.
- Experience with scripting and automation to execute test algorithms, and then analyze results with knowledge of lab equipment like scopes, Bidirectional Encoder Representations from Transformers (BERTs) and analyzers.
- Experience running and debugging the PCI Express protocol for root complexes and endpoints.
- Understanding of signal integrity, especially for high speed interfaces, and best practices to collect accurate SI data.
About the job
Googlers grow in focused teams and high-energy environments, believe in the ability of technology to change the world, and are as passionate about their lives as they are about their work. Google's custom-designed machines make up one of the largest computing infrastructures in the world. The Hardware Qualification team ensures that this equipment is reliable. In the Research and Development lab, you will plan for and execute the most effective way to test at scale.
In this role, you will lead initiatives to do end-to-end testing for machine learning hardware deployed in Google's data centers. You will give input on designs to improve our hardware until you're sure it meets Google's standards of quality, reliability, and security.
The ML, Systems & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
The US base salary range for this full-time position is $105,000-$151,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about .
Responsibilities
- Create, test, plan, and coordinate resources across test environments.
- Perform electrical, functional, performance, and reliability testing for Google's machine learning trays and solutions. Analyze results to ensure that they meet Google's requirements.
- Report bugs and motivate corrective action where needed with external suppliers and internal Supply Quality Engineer (SQEs), commodity teams, and developers.
- Use scopes, protocol analyzers, Bidirectional Encoder Representations from Transformers (BERTs), and other lab equipment to collect precision data.
- Organize results, communicate findings, and incorporate learnings to maintain and improve the qualification process.
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About The Company
A problem isn't truly solved until it's solved for all. Googlers build products that help create opportunities for everyone, whether down the street or across the globe. Bring your insight, imagination and a healthy disregard for the impossible. Bring everything that makes you unique. Together, we can build for everyone.
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