Software Engineer - Planner Frameworks Pipeline at zoox

<div> <p>Zoox is looking for an experienced software engineer to work on large-scale simulation pipelines used to validate the behavior of the Zoox self-driving vehicle. These are data and GPU intensive workloads built on Ray.io and Kubernetes. Given the massive scale and criticality of these pipelines, ensuring their reliability and efficiency has a significant impact on the company's ability to safely and quickly iterate on autonomy development.</p> <p>We are a small, scrappy team within the larger Autonomy organization. Although this role primarily involves off-vehicle pipelines, you will work closely with engineers developing the on-vehicle algorithms and models in our autonomy stack. We stay close to the end users - autonomy engineers - and think about the end to end use case for these validation pipelines.</p> <p>This is a hands-on role with a high degree of independence and ownership. You will be expected to contribute towards the framework’s architecture, reliability, efficiency, and grow its capabilities to support new use cases. You should have a track record of keeping production systems running with high availability. Experience with robotics or autonomous systems is not required but an understanding of the robotic data lifecycle is preferred.</p> </div> In this role, you will: • Improve the cost efficiency, reliability, and performance of our validation and simulation pipelines • Create production-grade APIs, SDKs, and tools to enable a varied set of validations of autonomous behaviors • Improve the ML training pipelines supporting the autonomous behavior org Qualifications • Bachelor’s degree in Computer Science or related field and 6+ years of industry experience • Experience optimizing large-scale distributed systems for cost and efficiency • Experience with AWS or similar providers • Proficiency with Python and familiarity with C++ Bonus Qualifications • Exposure to machine learning workloads (training, inference, data generation) from a cost optimization perspective • Background in algorithmic optimization or performance investigation • Experience with Ray.io, particularly Ray Core and Ray Data