Jobs at Goodie AI

Lead 0-to-1 AI product development for Goodie’s AI search platform. Define roadmap, work closely with engineering and design, engage enterprise customers, set quality metrics for AI output, use Mixpanel and customer signals to prioritize, and coordinate go-to-market with BD, CS, and Marketing.

Lead and execute Goodie’s growth engine end-to-end: define brand and product positioning, own AEO/SEO and paid performance, run rigorous experiments, build measurement and attribution (GA4), create automations and AI-driven workflows, and manage an agency partner. Report growth metrics to the CEO and translate analytics into actionable improvements to drive revenue.

Serve as primary strategic lead for enterprise clients to design AEO strategies across LLMs and AI search. Research AI models and prompt patterns, translate findings into competitive analyses and playbooks, integrate AEO with client growth teams, present insights to executives, and convert client challenges into product feedback.

Lead end-to-end product development for AI-driven marketing observability. Define roadmaps, prioritize features, coordinate engineering and design, use product analytics to measure performance, support go-to-market with sales/marketing, produce collateral, and iterate on customer feedback to ensure high-quality, usable AI products.

Build and run Goodies sales function: carry quota and close enterprise/mid-market B2B SaaS deals, run full-cycle sales, hire and coach AEs, define ICP and sales playbook, develop ROI narratives, own revenue strategy, and partner with leadership to align GTM and product based on field intelligence.

Lead development of Goodie AI's platform: architect and build React front-ends and backend services (Python/Node/Java), implement CI/CD and containerized deployments, integrate APIs and databases, collaborate with designers and data scientists, and optimize application performance and scalability.

Turn multi-model signals into measurement, forecasts, and optimizations. Clean and label large datasets, extract structure with LLMs/NLP, define product-grade metrics, run experiments, build predictive models, create evals and monitors, and translate insights into product and business decisions.