Data Center Quality Engineer, Machine Learning GPU Platforms
Job Summary
Job Description
- 8+ years experience in Quality/Manufacturing/Product Engineering of cloud hardware
- Experience with advanced statistical methodologies (DOE, SPC, Six Sigma)
- Data analysis and visualization skills (SQL, Python, etc.)
- Root cause analysis and problem-solving expertise
- Cross-functional team leadership
- Master's or PhD in relevant engineering field
- CRE/CQE certification
- Experience in manufacturing process setup and optimization
- Project management experience
- Executive communication skills
- Bonus
- Equity
- Benefits
10 skills required
Job Details
Minimum qualifications:
- Bachelor's Degree in Electrical Engineering, Computer Science, Computer Engineering, Mechanical Engineering, Industrial, Materials or other relevant engineering field or equivalent practical experience.
- 8 years of experience in Quality or Manufacturing or Product Engineering of cloud hardware technology or electronic hardware systems.
- Experience with advanced statistical (or statistics based) methodologies (e.g., design of experiments (DOE), statistical process controls (SPC), six sigma, FMEA, ANOVA, MSA, Fishbone or similar methodologies.
- Experience in data analysis and visualization using SQL, JMP, R, Matlab, Tableau, Power BI or Python+.
Preferred qualifications:
- Master's degree or PhD in Electrical, Mechanical, Industrial, Materials, or a related engineering field.
- Certification in CRE/CQE (Certified Reliability/Quality Engineer) or similar.
- Experience setting up manufacturing assembly processes, driving product launches and assembly process optimization within manufacturing operations.
- Experience leading cross-functional engineering teams using a practical and solution-oriented approach.
- Experience in technical leadership, project management, and executive communication.
- Ability to travel up to 20% of the time as needed.
About the job
This team is focused on implementing NonVisual Desktop Access (NVDA) platforms into our data center environment. In this role, you will be responsible for identifying problems and developing solutions related to this integration, as well as other system components. This includes writing basic code to prepare data and receiving various data metrics such as failure counts, temperature and cloud service. NVDA is a key focus, the role also involves understanding cable firmware and general system operations to diagnose the root cause across different components.
Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.
The US base salary range for this full-time position is $138,000-$202,000 + 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
- Provide technical leadership, set priorities, complete root cause analysis, and drive resolution of complex technical issues for robust product quality and predictable field deployment.
- Initiate, drive, and implement innovative product, process, tools development/improvement projects, separately and in a cross-functional environment.
- Gather and analyze manufacturing/field data within the data center environment to extract actionable insights and continuous improvement opportunities.
- Monitor, review and present product performance data and metrics to stakeholders in a concise and clear way that enables data-driven decision making.
- Manage multiple projects, some of which will be complex, lead cross-team initiatives, and identify/implement process improvements for the organization.
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