Sr. Staff Systems Engineer, Perception-Planner Risk

Waymo•Mountain View, CA

About The Position

Waymo is hiring a Sr. Staff Systems Engineer to define and advance quantitative safety risk analysis across sensing and perception systems engineering. The role builds on Waymo-wide risk assessment and management methodologies and develops sophisticated quantitative methods and statistical models to evaluate end-to-end performance for current and counterfactual sensor suites. The engineer will apply these approaches to rare events and events with limited operational data, assessing behavioral safety risks across situations such as pedestrian crowds, floods, and degraded sensing in construction zones. The role develops and prototypes workflows to determine required performance levels for sensing and perception tasks, inform perception hardware and software architecture decisions, and estimate exposure to events relevant to perception-driven behavioral risk. It also improves the rigor, standardization, execution efficiency, and reporting of end-to-end risk based on verification and validation outcomes in complex contexts, while expanding methodologies that support new operational design domains and emergent challenges. The engineer collaborates with Onboard Software and Evaluation, Test Engineering, and Systems Safety, as well as teams across Systems Engineering, Product, Simulation, and Data Science. The role communicates complex quantitative findings and recommendations to diverse stakeholders, including senior leadership, to support safety at scale.

Requirements

Bachelor’s degree or higher in computer science, mathematics, physics, engineering, or a related field.
10+ years of engineering experience, with demonstrated delivery of high-stakes technical roadmaps that resolved architectural friction and deployed business-critical systems.
Experience using AI productivity tools for analysis and workflow development.
Experience in vehicular robotics development or evaluation.
Software development proficiency in Python or C++.
Experience with statistical or predictive modeling.
Experience with experimental design and developing metrics for quantitative methods.
Deep technical knowledge of at least two of the following: state-of-the-art machine learning techniques powering modern perception systems and large end-to-end models; quantitative risk estimation using simulators, preferably with sensors-in-loop; systems engineering fundamentals such as requirements definition and formal verification and validation methods.
Track record of leading complex projects in safety-critical applications with leadership-facing accountability across software, operations, data science, product, and testing.
Excellent verbal and written communication skills and ability to manage by influence.

Nice To Haves

Master’s degree with thesis or PhD in machine learning, statistics, robotics, or a related quantitative field.
Autonomous driving industry experience.
Experience testing on large-scale infrastructure and deploying production-level safety-critical systems.
Track record of developing model-driven metrics that influence software or hardware design decisions at scale.

Responsibilities

Define the approach for quantitative safety risk analysis across sensing and perception systems engineering, building on Waymo-wide risk assessment and management methodologies.
Design, develop, and implement quantitative methods and statistical models to evaluate end-to-end performance for current and counterfactual sensor suites.
Apply the risk analysis approach to selected rare events or events with scarce operational data, including situations such as pedestrian crowds, floods, and degraded sensing in construction zones.
Develop and prototype workflows to determine required performance levels for sensing and perception tasks.
Develop workflows to support trade studies and architectural decisions for perception hardware or software.
Develop workflows to estimate exposure to events relevant to perception-driven behavioral risk.
Improve quantitative rigor, standardization, execution efficiency, and reporting of end-to-end risk from V&V test outcomes in complex contexts.
Expand existing methodologies to support new operational design domains and address emergent challenges.
Collaborate with Onboard Software and Evaluation to map perception and planner evaluation to end-to-end risk in large models.
Collaborate with Test Engineering to cross-calibrate risk from V&V results across methodologies and coverage competencies, and provide actionable data to improve test tooling across public-road and closed-course tests and simulations using logged or synthetic scenarios.
Collaborate with Systems Safety on appropriate allocation of quantitative risk across functional insufficiencies.
Communicate complex quantitative findings and recommendations to diverse stakeholders, including senior leadership.
Collaborate within Systems Engineering and cross-functionally with Product, Simulation, and Data Science teams.