Job description

The Behavior Capabilities opportunity falls within the Fail Operational Planning team at Zoox. This team works to ensure that the robot continues to operate effectively, even in the presence of minor compartmentalized failures or degraded capabilities. The focus of the team includes developing behavioral and motion planning algorithms to improve trip progress, vehicle uptime metrics, and correspondingly a better customer experience. This is a cutting-edge problem which requires teamwork and collaboration across a diverse group of experts including Planner, Perception, Prediction, Hardware, and more. On this team, you will contribute to the next generation of algorithmic advancements to expand our robot’s ability to drive in some of the most complex scenarios. We are looking for an engineer who is excited to understand the interdependencies within the driving stack and takes ownership of their projects and has a holistic product focused approach towards development. Responsibilities Create principled algorithmic improvements to specific areas of driving behavior and implement those changes in our code base. Collaborate cross-functionally to develop strategies and intelligent behaviors to mitigate the performance degradation under various failure modes of the system. Architect and implement scalable solutions ensuring seamless integration with the rest of the driving stack. Partner with validation teams to define and execute tests for developed algorithms. Qualifications Master's degree in Computer Science or related field Fluency in C++ Extensive experience with programming and algorithm design Understanding of vehicle kinematics 4+ years of experience in a related field Bonus Qualifications PhD in Computer Science, Electrical Engineering, Mechanical Engineering, or related field Experience shipping products to market (commercial robotics, drones, consumer devices etc.) Demonstrated ability to create real-time motion planning algorithms in a cross-functional team About Zoox Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team. Follow us on LinkedIn Accommodations If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter. A Final Note: You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

Requirements

Must-haves

Create principled algorithmic improvements to specific areas of driving behavior and implement those changes in our code base.
Collaborate cross-functionally to develop strategies and intelligent behaviors to mitigate the performance degradation under various failure modes of the system.
Architect and implement scalable solutions ensuring seamless integration with the rest of the driving stack.
Partner with validation teams to define and execute tests for developed algorithms.
Master's degree in Computer Science or related field
Fluency in C++
Extensive experience with programming and algorithm design
Understanding of vehicle kinematics
4+ years of experience in a related field
PhD in Computer Science, Electrical Engineering, Mechanical Engineering, or related field
Experience shipping products to market (commercial robotics, drones, consumer devices etc.)
Demonstrated ability to create real-time motion planning algorithms in a cross-functional team

Nice-to-haves

Bonus Qualifications

Application prep

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

Keywords to mirror

restmachine learning

Platform: lever
Effort: quick