Staff Field Quality Engineer

Hardware Engineering is an innovative and collaborative group of electrical, mechanical, reliability, software and vehicle engineers. We design, build, and perfect the products which are the eyes and ears of Waymo's autonomous driving technology, and integrate those products into vehicle platforms. We're seeking curious and talented teammates to keep us moving in the right direction.

This role follows a hybrid work schedule and reports to the hardware field quality lead.

You will:

• Champion field quality initiatives, collaborating with Engineering, Manufacturing, Supply Chain, Software, and Operations teams to enhance product quality and reliability throughout the product lifecycle.
• Analyze fleet data to proactively identify emerging quality trends and systemic risks.
• Lead cross-functional root cause analysis, containment, and resolution for complex field issues, ensuring corrective actions prevent recurrence.
• Develop scalable field quality processes, tools, and automation to effectively identify, triage, and mitigate field failures.
• Define quality coverage requirements and implement validation methods to ensure corrective actions are effective in the field.
• Establish a structured feedback system where data and lessons learned continuously improve designs, diagnostics, and operational playbooks.

You have:

• A Bachelor’s degree in an Engineering discipline.
• 8 years of experience in electro-mechanical product development.
• Experience in a quality, reliability, or similar engineering role.
• Experience in data analysis, reporting, and dashboard creation using tools such as Python, SQL, JMP, or Tableau.
• Strong knowledge and practical application of core quality and reliability methodologies and tools (e.g., 8D DMAIC, Kepner-Tragoe, FMEA, MSA, SPC, DOE). non highlighted- other problem solving tools
• Proven ability to lead cross-functional teams, coupled with excellent communication and problem-solving skills.

We prefer:

• A Master’s degree or higher in an Engineering discipline.
• Experience in the automotive, robotics, or similar complex systems industry.
• Advanced experience with statistical modeling and large-scale data analysis tools (e.g., Tableau, JMP, PLX).
• Deep understanding of hardware/software interactions, embedded systems, and diagnostics.
• Familiarity with advanced reliability analysis techniques (e.g., Weibull analysis, Nevada charting).