Jobs at Goodie

Own full enterprise sales cycle for Goodie AI: qualify, multi-thread, design POV pilots, negotiate contracts, and close deals. Build outbound ABM plays, forecast pipeline in HubSpot, collaborate with Product/Data/Solutions, and feed market intel to shape product and go-to-market narratives.

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.

Join Goodie AI's AI Lab to design and build scalable front-end solutions and contribute to backend infrastructure and DevOps. Lead React development, collaborate with designers and data scientists, integrate APIs, ensure performance, and participate across the full application lifecycle to deliver high-quality AI platform features.

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.

Lead development and launch of AI-driven marketing products for a B2B SaaS observability platform. Define roadmaps, prioritize features, coordinate engineering and design, use product analytics to drive decisions, support go-to-market and marketing collateral, and iterate based on market and customer feedback.

Design end-to-end B2B SaaS product experiences: user research, wireframes, prototypes, high-fidelity UI, design systems, usability testing, and cross-functional collaboration to drive product and UX outcomes.

Lead full-stack development of an AI platform: design scalable front-end React apps, build performant back-end services, implement DevOps/CI-CD, integrate APIs and databases, collaborate with designers and data scientists, and maintain product performance and security.

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.