Senior/Staff Software Engineer - Planner Frameworks Pipeline

Zoox; Foster City (CA); On-site; USD 254,000 - 350,000; Full time

Benefits offered by this job

Health insurance

Long-term care insurance

Disability insurance

Life insurance

Zoox Stock Appreciation Rights

Amazon RSUs

Sign-on bonus

Paid time off

Unpaid time off

Job summary

Zoox is hiring an experienced software engineer to work on large-scale simulation pipelines for validating autonomous vehicle behavior. The role involves data- and GPU-intensive workloads built on Ray.io and Kubernetes, with collaboration across autonomy and validation groups to ensure end-to-end use-cases.

A hands-on role with ownership and system reliability expectations. Required are 8+ years in CS, expertise in distributed systems, AWS, Python, and C++.

Qualifications

• Bachelor's degree in Computer Science or related field and 8+ 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++.

Responsibilities

• 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

Skills

Python

Distributed systems

Ray.io

Education

Bachelor's degree in Computer Science or related field

Tools

AWS

Job description

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 8+ 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

$254,000 - $350,000 a year

Base Salary Range

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

• paid time off (e.g. sick leave, vacation, bereavement)
• unpaid time off
• Zoox Stock Appreciation Rights
• Amazon RSUs
• health insurance
• long-term care insurance
• long-term and short-term disability insurance
• life insurance

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.