Lead the technical direction and end-to-end delivery of advertiser verification products and systems, from problem definition and architecture through implementation and rollout
• Set direction and goals for teams, lead major initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
• Combine trusted signals, user-provided evidence, deterministic systems and AI/ML capabilities to improve verification quality and adapt the experience to different needs and risk levels
• Build intuitive user experiences and scalable backend services that help people and businesses establish their identity and demonstrate their connection to a business
• Establish testing, evaluation, monitoring, and rollout safeguards for production systems, including AI-enabled features
• Improve system reliability, performance, and scalability, and address technical debt that limits product development
• Provide technical guidance and mentorship, lead design and code reviews, and build alignment across teams while remaining hands-on in engineering delivery
• Establish ownership of components, features, or systems with expert end-to-end understanding
• Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences
Minimum Qualifications
• 8+ years of programming experience in a relevant language OR 4+ years experience with a PhD
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Programming experience in a relevant language
• Demonstrated experience driving change within an organization and leading complex technical projects
• Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions
Preferred Qualifications
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience building identity, authentication, verification, fraud prevention, or trust and safety products; or working on adversarial problems where misuse evolves over time
• Experience building detection, classification, or enforcement systems for abuse, fraud, or safety harms at consumer scale
• Experience in trust and safety, risk, fraud, security, or regulated-domain engineering
• Experience evaluating and addressing privacy, security, accuracy, and potential bias in data- or AI-enabled systems
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience applying machine learning or LLM-based capabilities to production products, including evaluation, monitoring, fallback behavior, and quality improvements
• Experience working on adversarial problems where bad actors actively adapt to countermeasures
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience developing reliable, scalable applications and improving quality through code reviews, testing, safe rollout, and proactive monitoring
