Job description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. 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. 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. The Onboard Software team develops the critical software and machine learning models that power the Waymo Driver. Our systems enable the vehicle to perceive its surroundings, make safe driving decisions, and transport people to their destinations. With Waymo's fleet driving millions of miles every week, we operate at a scale that exposes our technology to a vast array of challenging real-world scenarios. As the Onboard Field Response Lead, you will play a key role in our continuous improvement cycle. Your primary responsibility will be to lead the engineering analysis and response to novel, rare, or unexpected events encountered by the fleet in the field. This includes rapidly improving the Waymo Driver in some of the most challenging driving situations we encounter, participating in high-judgment decisions given new driving situations, and proactively evolving the Onboard Software system to enhance its robustness and adaptability to these really challenging situations. This unique role blends the technical complexity of AI with the real-world complexity and impact of driving at scale. The challenges of ensuring field performance for a distributed, mobile, real-world system like ours are unprecedented. If you are a software engineering leader with experience leading a large-scale production system who is passionate about solving first-of-their-kind problems with significant real-world impact and be hands on, we'd like to meet you. This role follows a hybrid work schedule and reports to the head of Driver Refinement and Validation within the Onboard organization. You will: Lead the engineering analysis and response to novel, rare or unexpected events encountered by the fleet in the field. Contribute to Onboard software architecture to help improve its robustness and debuggability. Harden software, develop playbooks, improve oncall metrics and post-mitigation workstreams, and improve robustness of the software system running inside the autonomous vehicle. Partner with organization leads and others across the Onboard organization to improve large-scale software engineering best practices. Organize and participate in oncall rotations for how to respond to driving issues in the field. If needed: request, hire, and manage additional engineers to contribute to the above responsibilities. You have: Master's degree or PhD in Computer Science, Engineering, or a related technical field 10+ years of experience leading large-scale production software systems, especially machine learned systems 5+ years of experience as an organizational leader, managing a group of people and partnering with other organizational leaders Excellent influence without authority skills Experience organizing and running oncall rotations Organizational awareness, extremely collaborative, strong communication, focus on value add to the company and to the team We prefer: Experience leading hybrid hardware/software production systems Experience leading teams who work on modern deep ML / large scale models Track record of bringing about change in 1000+ person engineering organizations 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 $349,000 — $431,000 USD

Requirements

Must-haves

This role follows a hybrid work schedule and reports to the head of Driver Refinement and Validation within the Onboard organization.
Lead the engineering analysis and response to novel, rare or unexpected events encountered by the fleet in the field.
Contribute to Onboard software architecture to help improve its robustness and debuggability.
Harden software, develop playbooks, improve oncall metrics and post-mitigation workstreams, and improve robustness of the software system running inside the autonomous vehicle.
Partner with organization leads and others across the Onboard organization to improve large-scale software engineering best practices.
Organize and participate in oncall rotations for how to respond to driving issues in the field.
If needed: request, hire, and manage additional engineers to contribute to the above responsibilities.
Master's degree or PhD in Computer Science, Engineering, or a related technical field
10+ years of experience leading large-scale production software systems, especially machine learned systems
5+ years of experience as an organizational leader, managing a group of people and partnering with other organizational leaders
Excellent influence without authority skills
Experience organizing and running oncall rotations
Organizational awareness, extremely collaborative, strong communication, focus on value add to the company and to the team
Experience leading hybrid hardware/software production systems
Experience leading teams who work on modern deep ML / large scale models
Track record of bringing about change in 1000+ person engineering organizations

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaiml

Platform: greenhouse
Effort: quick