Software Engineer - Collision Avoidance System Metrics

Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.

The Collision Avoidance System (CAS) is responsible for detecting and reacting to imminent collision situations in support of our vehicle’s overall safety goals. CAS Perception is responsible for processing raw sensor data from our vehicle’s world-class sensor suite using a combination of geometric, interpretable algorithms and deep learning to detect near-collisions with obstacles along our intended driving path, in the most challenging dense urban environments and under tight compute resource constraints. Overall CAS is parallel and complementary to our Main Artificial Intelligence (AI) autonomy stack, and has a close relationship with our vehicle hardware and safety teams in order to architect redundancy into our overall driving system.

The CAS Verification & Validation (CAS V&V) is a multidisciplinary team data, software and systems engineers defining and building metrics to measure the Collision Avoidance System performance and work with the Systems Design and Mission Assurance (SDMA) and QA teams to develop validation plans for the features.

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.

If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.

Apply distributed computing algorithms to analyze petabytes of urban driving data.

Develop metrics and tools to analyze errors and system improvements.

Work closely with CAS engineers to evaluate system performance.

Collaborate with Perception engineers to define metrics for autonomous driving.

Partner with Planning engineers to measure performance in complex urban environments.

BS, MS, or PhD degree in computer science or a related field

Fluency in C++ and/or Python

Extensive experience with programming and algorithm design

Experience with analysis of latency for safety-critical software systems

Experience with petabyte-scale distributed computing (Spark, Databricks, generic MapReduce pipelines)

Background in Bayesian statistics