Staff Software Engineer - Autonomous Driving Fault-Discovery

Waymo; San Francisco (CA); On-site; USD 238,000 - 302,000; Full time

Benefits offered by this job

Discretionary annual bonus

Equity incentive plan

Generous benefits program

Job summary

Waymo is seeking a Tech Lead for the software-fault release evaluation team to oversee the virtual driver’s test system and ensure reliable fault discovery for the Waymo Driver. You will guide development of the end-to-end simulation framework used to identify issues before deployment.

You will collaborate with Data Science and Systems Engineering to improve signal quality and predictive validity of test results, and contribute to model-based evaluations and feature roadmaps for reliable

Qualifications

• 7+ years of full-time software engineering experience, or a quantitative PhD with 2+ years of professional software engineering experience
• C++ proficiency
• Python familiarity
• SQL familiarity
• Strong verbal and written communication skills
• Excited about autonomous driving, Sim+Eval, eager to learn

Responsibilities

• Oversee and contribute to ongoing operations and administration of the virtual driver's test to support Waymo’s software deployment schedule.
• Make strategic investments to measure and improve the signal quality provided by the virtual driver's test; collaborate with partner teams.
• Align system design for the fault-discovery system with release simulation and evaluation practices; surface key requirements.
• Apply ML models to improve efficiency of the test; simulate scenarios and report results.
• Partner with Data Science and Systems Engineering teams for analysis and understanding of Waymo Driver internals.

Job description

Waymo is seeking a Tech Lead for the software-fault release evaluation team to oversee the virtual driver’s test system and ensure reliable fault discovery for the Waymo Driver. You will guide development of the end-to-end simulation framework used to identify issues before deployment.

You will collaborate with Data Science and Systems Engineering to improve signal quality and predictive validity of test results, and contribute to model-based evaluations and feature roadmaps for reliable