Software Engineer - Planner Frameworks Pipeline
Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on May 12, 2026. Zoox scores B on the Alion truth index.
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.
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.
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.
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