Staff Systems Test Engineer - Scale Quality & Automation

Waymo

San Francisco (CA)

Hybrid

USD 230,000 - 284,000

Full time

14 days+
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Benefits offered by this job

Discretionary annual bonus
Equity incentive plan
Generous benefits

Job summary

Waymo is seeking a Systems Engineering professional for a Hybrid role focused on scalable quality architecture and test health across our driver platform. You will design QC methodologies, build meta-quality dashboards, and drive automation to scale testing in our scenario database.

You will lead testing programs, collaborate with SMEs, and guide vendor-driven resolution, leveraging Python/C++ skills and data analysis to improve first-pass quality in a fast-moving autonomous driving program.

Qualifications

  • Bachelor’s degree in Engineering, Computer Science, Robotics, or a related technical field.
  • 8+ years of experience in Test Engineering, QA Engineering, or related fields.
  • Strong debugging and Python/C++ coding skills, and SQL or data analysis experience.
  • Experience architecting testing programs at scale and leading test automation strategies.
  • Ability to thrive in a high-ambiguity, dynamic environment.

Responsibilities

  • Design scalable QC architectures for test creation and automated validation.
  • Manage meta-quality assurance, dashboards, and first-time accuracy improvements.
  • Maintain test health with automation to reduce rework and downtime.
  • Create playbooks for maintenance at scale with tooling and SME collaboration.

Skills

Python
C++
Debugging
Test automation

Education

Bachelor's degree in Engineering/CS/Robotics

Tools

SQL
Data analysis tools
Automation tooling

Job description

Waymo is seeking a Systems Engineering professional for a Hybrid role focused on scalable quality architecture and test health across our driver platform. You will design QC methodologies, build meta-quality dashboards, and drive automation to scale testing in our scenario database.

You will lead testing programs, collaborate with SMEs, and guide vendor-driven resolution, leveraging Python/C++ skills and data analysis to improve first-pass quality in a fast-moving autonomous driving program.

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