Avride
QA Engineer – AV Behavior Simulation Testing
Austin, Texas
Work authorization requiredDetected 1 day ago
PythonSensors
About the role
- Your analytical mindset and proactive approach to continuous improvement will be key to your success in this role.
- Our Simulation Testing team ensures reliability and safety of autonomous driving systems through comprehensive virtual scenario testing.
- Our main goal is to detect and resolve issues as early and thoroughly as possible, prior to testing in real-world conditions.
Responsibilities
- Design and implement structured testing plans for new features and bug fixes
- Develop detailed checklists and testing scenarios
- Analyze test outcomes and document defects clearly
- Create reports on test findings and safety metrics
- Design metrics for evaluating AV performance and safety
- Proactive, inquisitive mindset with a drive for continuous improvement and deeper product understanding
Requirements
- 3+ years of experience in software testing
- Excellent ability to plan and prioritize tasks effectively under varying workloads
Nice to have
- Experience with autonomous vehicle testing
- Proficiency in at least one programming language (e.g., Python)
- Basic understanding of vehicle dynamics or sensor technology (LiDAR, Radar, Cameras)
- Avride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities.
Benefits
- Collaborate closely with cross-functional teams to improve test coverage and effectiveness
Company info
- We collaborate closely with the Motion Planning, Prediction, Perception, and Control teams to build effective offline testing environments, reliable testing processes, and datasets.
Visa & Work Authorization
- Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.
This listing is sourced directly from Avride's careers page and normalized into a canonical job model.