Waymo
Field Quality Engineer
Mountain View, CA, USA · Full-time
Sponsorship not specified$177k-$218kDetected 14 days ago
PythonSQLData AnalysisData VisualizationStatisticsProduct ManagementSupply ChainEmbedded SystemsRoboticsHardware DesignCommunicationProblem Solving
About the role
- The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases.
- The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.
- Hardware Engineering is an innovative and collaborative group of electrical, mechanical, reliability, software and vehicle engineers.
Responsibilities
- Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver™-to improve access to mobility while saving thousands of lives now lost to traffic crashes.
- 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.
Requirements
- A Bachelor's degree in an Engineering discipline.
- 5+ years of experience in electro-mechanical product development.
- 5+ years of experience in a quality, reliability, or similar engineering role.
- 5+ years of experience in data analysis, reporting, and dashboard creation using tools such as Python, SQL, JMP, or Tableau.
- A Master's degree or higher in an Engineering discipline.
- Advanced experience with statistical modeling and large-scale data analysis tools (e.g., Tableau, JMP, PLX).
Skills
- Analyze fleet data to proactively identify emerging quality trends and systemic risks.
Compensation
- $177,000 - $218,000 USD
Company info
- Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver.
This listing is sourced directly from Waymo's careers page and normalized into a canonical job model.