Bachelor's or higher degree in computer science, math, physics, engineering, or a related field
10+ years of engineering experience with high-stakes technical roadmaps
Experience using AI productivity tools for analysis and workflow development
Proficiency in vehicular robotics, software development (Python or C++), statistical modeling, and experimental design
Deep technical knowledge of ML techniques, quantitative risk estimation, and systems engineering fundamentals
Track record of leading complex safety-critical projects with accountability across multiple domains
Excellent verbal and written communication skills, with the ability to manage by influence
Responsibilities
Define quantitative safety risk analysis approaches for sensing and perception systems
Apply risk assessment methods to rare events with limited operational data
Develop workflows to analyze performance levels of sensing tasks
Support architectural trade studies for perception hardware or software
Estimate exposure to perception-driven behavioral risks
Collaborate with cross-functional teams to map evaluation to end-to-end risk
Communicate complex findings to stakeholders to enable safety at scale
Benefits
Participation in Waymo's discretionary annual bonus program
Equity incentive plan
Generous Company benefits program, subject to eligibility requirements
Full Job Description
You will:
Define the approach for quantitative safety risk analysis across sensing and perception systems engineering, building on top of Waymo-wide risk assessment and management methodologies. You will design, develop, and implement sophisticated quantitative methods and statistical models for evaluating end-to-end performance for current and counterfactual sensor suites.
Apply the defined approach for selected events that occur rarely or have scarce operational data. The generalized goal is to assess behavioral safety risk across a wide variety of situations like interacting with pedestrian crowds, navigating around floods, and degraded sensing in construction zones.
Develop and prototype novel workflows to:
Analyze to determine the required performance levels of various sensing and perception tasks
Support trade studies to inform architectural decisions for perception hardware or software
Estimate exposure of events relevant to perception-driven behavioral risk
Improve quantitative rigor, standardization, efficiency of execution, and reporting of end-to-end risk from V&V test outcomes in complex contexts.
Expand existing methodologies that are crucial for breaking into new operational design domains and addressing emergent challenges
Collaborate closely with:
Onboard Software and Evaluation for mapping perception evaluation and planner evaluation to end-to-end risk in large models
Test Engineering for cross-calibrating risk from V&V results across various methodologies and coverage competencies, and providing actionable data to improve Waymo's test tooling across public roads/closed course tests and simulations on logged/synthetic scenarios
Systems Safety for appropriate allocation of quantitative risk across functional insufficiencies
Clearly communicate complex quantitative findings and recommendations to diverse stakeholders, including senior leadership, to enable safety at scale. You will collaborate deeply within SysEng and cross-functionally with Product, Simulation, and Data science teams to advance safely and deliver excellence.
You have:
Bachelors or higher degree in computer science, math, physics, engineering, or a related field
10+ years of engineering experience with demonstrated delivery of high-stakes technical roadmaps that successfully resolved architectural friction and deployed business-critical systems
Experience using AI productivity tools for analysis and workflow development
Proficiency and experience in:
Vehicular robotics (development or evaluation)
Software development (Python or C++)
Statistical or predictive modeling
Experimental design and developing metrics for quantitative methods
Deep technical knowledge of at least two of:
State of the art ML techniques that power 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, 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
We prefer:
Masters (with thesis) or PhD in machine learning, statistics, robotics, or a related quantitative field
Autonomy 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
The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.
Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.
Salary Range
$272,000-$336,000 USD
About Waymo
Waymo LLC is a self-driving technology development company. It is a subsidiary of Alphabet Inc., the parent company of Google. Waymo develops autonomous driving technology and provides ride-hailing services through its Waymo One program. The company has been testing its self-driving technology on public roads since 2009 and has logged millions of miles of autonomous driving. Waymo has partnerships with several automakers and has been working on developing autonomous trucks for use in logistics.
