• Define Technical Strategy: Own the long-term technical roadmap • Architect Scalable Systems: Lead architectural transition • Drive Technical Excellence: Act as the final authority on website infrastructure • Engineering Partnership: Serve as the primary technical liaison • Build-to-Enable: Create reusable components and primitives • Performance & Security Oversight: Own the 'Definition of Done' • Experimentation Infrastructure: Lead technical implementation of A/B testing • Technical Mentorship: Provide architectural guidance and rigorous code reviews • Strategic Advising: Act as the senior technical partner for Marketing leadership

• Shape and evolve engineering standards and shared technical patterns across teams. • Ensure products and services maintain high quality, reliability, and measurable value • Mentor and coach less experienced engineers across the Engineering team • Identify, track, and report on key quality measures of product engagement and value • Provide architectural leadership to empowered product teams, guiding the design and evolution of high-quality, scalable systems.

• Contribute to and lead feature implementations in a collaborative environment, accounting for open-source community participation where appropriate. • Be responsible for product quality, participate in peer code reviews, continuous integration (CI), and secure engineering. • Coordinate with team leads, architects, and other engineers on solution design and architecture. • Participate in Agile development practices and, where appropriate, educate the team on new features through sprint reviews and demos. • Help shape key technical decisions for the platform.

• Design, implement, and ship AI-driven features and systems into production environments • Lead technical decision-making for AI architecture, data modeling, and system integration within your domain • Partner closely with Product, Engineering, and other stakeholders to translate business needs into scalable technical solutions • Identify and address reliability, scalability, performance, and observability challenges in AI systems • Implement and advocate for best practices for applied AI engineering, including agent evaluation, interpretability, data layer design, and monitoring • Mentor and guide other engineers, raising the bar on AI engineering quality and decision-making • Provide input into technical strategy and roadmap discussions related to AI capabilities and platform evolution