• Design, develop, and maintain a frontend generative AI web app (React/NextJS/Typescript) and related backend services (FastAPI/Python) • Develop and maintain RESTful APIs for seamless integration with frontend applications • Collaborate closely with machine learning engineers to translate prototypes into production-ready systems • Participate in all phases of the software development lifecycle, including design, implementation, testing, and deployment

• Obsess over the customer to build pragmatic solutions to their problems • Lead end-to-end implementation of features, from discovery through implementation • Design software that solves real-world logistics challenges—often involving both automation and human-in-the-loop workflows • Architect and implement core infrastructure that helps us scale both customer and mail volume • Balance technical complexity with input from customers, operations, and the business

• Build the Marketing System: Stand up the core GTM tech stack (automation, attribution, CRM integrations) and establish scalable processes from the ground up. • Growth Experiments: Run scrappy, high-velocity tests (A/B, messaging pilots, channel experiments) to validate positioning and accelerate adoption. • Messaging & Content: Translate technical AI features into compelling narratives—decks, demos, one-pagers, and case studies. • Campaign Design & Execution: Launch end-to-end GTM campaigns (email sequences, paid/social, webinars, landing pages) to generate awareness and pipeline. • Analytics & Insights: Define success metrics (CAC, LTV, funnel conversion), build dashboards, and refine strategies with data. • Cross-Functional Collaboration: Partner with product, sales, and ops to ensure GTM programs align with ICPs, sales workflows, and customer adoption.

• Translate business logic into high-quality code, adhering to established standards and best practices • Participate in defining and implementing new features and fixing bugs • Understand and discuss problems and proposed solutions, always seeking the best technical approach to the challenges faced • Review code delivered by team members • Break down large deliveries into cohesive, smaller deliverables • Monitor implemented solutions in production, assessing errors, performance, and availability • Contribute to improvements or new standards in the Engineering knowledge base • Identify and resolve structural issues in product code (cross-cutting concerns) • Monitor application telemetry and implement performance improvements • Implement reusable components and abstractions for engineering teams • Provide technical consulting to engineering teams on technical decisions • Communicate technical decisions based on architectural trade-offs