Software Quality Operations Specialist, Safety Evaluation

About this role

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Skills you'll use

What you'll do

• Provide technical leadership and consultation to stakeholders to enhance our workflows and quality. You'll be at the forefront of identifying and escalating issues with our tools, providing technical requirements to engineering, and driving user testing to support the development and deployment of new tooling features.
• Act as the subject matter expert for our Software Quality Operations, working closely with stakeholders, program leads, and vendor teams to ensure seamless adoption and maximum impact of AI/ML advancements in our quality processes. Be the trusted source for creating and updating technical policies, guidelines, and standard operating procedures for new scopes, platforms, and driving signals
• Design and implement robust quality control processes for both human and AI-generated outputs. Perform meta-quality checks, validate the integrity of vendor work, and provide feedback to improve both human and model performance.
• Partner with Engineering to design, test, and deploy cutting-edge Machine Learning (ML) and Generative AI (Gen-AI) models and tools to drive improvements in issue discovery & detection, triage efficiency, and quality assurance.
• Leverage AI-powered insights and traditional triage signals to proactively identify emerging on-road issue trends, new risk scenarios, and edge cases. Develop and refine data-driven strategies for issue discovery and monitoring, enhanced by ML model outputs
• Serve as the key link between AI/ML development and operational execution. Define and document new policies, guidelines, and Standard Operating Procedures (SOPs) that integrate AI tools and insights into daily vendor workflows.
• Scale workflow from 16/5 to 24/7 coordinating with external stakeholders, vendor team, and FTE team

Who can apply

• BS/BA degree and 4 years of relevant work experience in QC or AV Software Quality Operations
• Strong understanding of driving rules and regulations, as well kinematic principles critical to safety risk assessment
• Increased competency in supporting all phases of the machine learning development lifecycle: data preparation, training, validation, deployment, and continuous monitoring.
• Ability to quickly learn and implement new concepts and utilize proprietary tools.
• A proven ability to work in a fast-paced, high-stress environment while maintaining good judgment
• Excellent communication and interpersonal skills to effectively collaborate with a wide range of individuals in a diverse and dynamic work environment
• Demonstrated strong execution with ability to drive outcomes
• Ability to effectively and efficiently communicate with cross-functional stakeholders

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