Autonomy Systems Test Engineer

As an Autonomy Systems Test Engineer at Zoox, your key responsibilities will include creating test strategies for the self-driving features, overseeing extensive test planning, and collaborating with software development teams to track and resolve issues. You'll also design and execute synthetic test scenarios and compile results from various QA validation platforms to refine our testing processes effectively.

To be considered for the Autonomy Systems Test Engineer position at Zoox, candidates should have a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field. Additionally, having more than 3 years of experience in designing scalable software testing strategies, familiarity with automated test frameworks, and proficiency in scripting languages like Python are essential qualifications.

At Zoox, Autonomy Systems Test Engineers develop innovative testing methodologies, particularly for AI applications, utilizing both automated metrics and synthetic test scenarios within our in-house simulation frameworks. This approach helps to validate software behavior in numerous situations, ensuring that our autonomous vehicles operate safely and efficiently.

Candidates for the Autonomy Systems Test Engineer role at Zoox should be proficient with tools like JIRA and GIT for project management and version control, as well as the Linux command line environment. Experience with Python or other scripting languages is also necessary to develop and execute automated test scripts.

The salary for an Autonomy Systems Test Engineer at Zoox ranges from $125,000 to $175,000, depending on various factors such as experience, geographic location, and the level for which the candidate is applying. Additionally, compensation may include stock options and other benefits.

When developing test plans for autonomous driving features at Zoox, I would begin by collaborating with cross-functional teams to understand the specific functionalities we need to test. I would prioritize test scenarios based on risk and impact, making sure to account for edge cases and real-world driving conditions. By leveraging simulation tools and structured test methodologies, I would ensure comprehensive coverage and iterative refinement of our test plans.

In my previous roles, I have implemented and utilized automated testing frameworks to enhance efficiency and reduce manual testing efforts. I can discuss specific projects where I developed automated tests for various functionalities, streamlined testing processes, and analyzed the outcomes to identify areas for improvement in the software's performance.

When a test fails, my approach to root cause analysis involves a systematic investigation. I start by replicating the error under controlled conditions, gathering logs, and reviewing metrics to gain insights into the failure. I collaborate with developers to understand the code involved, ensuring we dissect the issue from both a software and a testing perspective, which often leads to quicker and more effective solutions.

In a software development context, tools like JIRA for tracking issues and progress, Git for version control, and communication platforms like Slack or Microsoft Teams are essential. These tools facilitate transparent communication and effective collaboration, keeping everyone aligned on project goals and progress.

In a previous project, we faced tight deadlines to roll out a new software feature. I coordinated with my team to draft a series of essential test cases, prioritizing those that would impact functionality most severely. By utilizing automated testing tools and working overtime, we were able to meet the deadline without compromising on quality, which involved diligent retesting and thorough documentation of any issues discovered post-deployment.

To prevent regression, I advocate for maintaining a comprehensive suite of regression tests that are automatically run with each new build. I ensure these tests cover critical functionalities, and I continuously update them based on user feedback and newly introduced features. Having regular code reviews and engaging in continuous integration practices also significantly reduces the chances of regression.

Documentation is vital in the testing process. It serves as a record that guides future testing efforts and helps onboard new team members. I maintain detailed logs of test plans, results, issues encountered, and resolutions, as this not only aids in accountability but also ensures that knowledge is preserved within the team.

One challenging test scenario involved simulating a variety of unpredictable driving behaviors in an urban environment. By designing synthetic scenarios that replicated real-life complexities, such as sudden lane changes by other drivers or unexpected traffic patterns, we were able to assess how our autonomous vehicle's software handled these situations, ultimately enhancing its adaptability and safety.

Staying updated in the fast-evolving field of autonomous vehicle testing is crucial. I regularly read industry publications, attend relevant webinars, and participate in professional forums. Networking with other professionals in the field also provides valuable insights into the latest trends and best practices, ensuring I remain informed about advancements in technology and methodologies.

Key metrics for assessing the effectiveness of a test strategy include defect density, test coverage percentage, and the ratio of detected defects to fixes. Monitoring these metrics can provide insights into areas of improvement within the testing process, help identify trends over time, and influence strategic decisions on resource allocation for future testing endeavors.