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Описание вакансии
Текст:
TL;DR
Staff Field Quality Engineer (Automotive): Improving the field quality and reliability of autonomous driving hardware through fleet-data analysis, cross-functional root cause analysis, and scalable validation processes with an accent on electro-mechanical products, diagnostics, and corrective action effectiveness. Focus on identifying systemic risks, automating field-failure triage, and building feedback systems that improve designs and operational playbooks.
Location: Mountain View, California, United States; hybrid work schedule.
Salary: $210,000–$259,000 USD per year, plus eligibility for an annual bonus, equity incentive plan, and company benefits.
Company
Autonomous driving technology company developing autonomous ride-hail services and vehicle-platform solutions through its self-driving system.
What you will do
- Lead field quality initiatives across engineering, manufacturing, supply chain, software, and operations.
- Analyze fleet data to identify emerging quality trends and systemic reliability risks.
- Lead cross-functional root cause analysis, containment, and corrective action for complex field issues.
- Develop scalable quality processes, tools, and automation for identifying, triaging, and mitigating field failures.
- Define quality coverage requirements and validation methods to verify corrective actions in the field.
- Build structured feedback systems that improve product designs, diagnostics, and operational playbooks.
Requirements
- Bachelor’s degree in an engineering discipline.
- 8+ years of experience in electro-mechanical product development.
- 5+ years of experience in quality, reliability, or a similar engineering role.
- 5+ years of experience with data analysis, reporting, and dashboard creation using Python, SQL, JMP, or Tableau.
- Practical knowledge of quality and reliability methods including 8D, DMAIC, Kepner-Tregoe, FMEA, MSA, SPC, and DOE.
- Ability to lead cross-functional teams with strong communication and problem-solving skills.
Nice to have
- Master’s degree or higher in an engineering discipline.
- Experience in automotive, robotics, or other complex-systems industries.
- Advanced statistical modeling and large-scale data analysis experience with tools such as Tableau, JMP, or PLX.
- Understanding of hardware/software interactions, embedded systems, and diagnostics.
- Experience with advanced reliability analysis techniques such as Weibull analysis and Nevada charting.
Culture & Benefits
- Collaborative work across electrical, mechanical, reliability, software, and vehicle engineering disciplines.
- Hybrid work schedule.
- Discretionary annual bonus program, subject to eligibility.
- Equity incentive plan, subject to eligibility.
- Company benefits program, subject to eligibility.
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