Improvado is an AI-powered, unified platform designed for marketing teams in medium to large-scale enterprises and agencies, who are looking to automate complex marketing intelligence and reporting to make decisions with ease. Improvado gathers, organizes, and untangles marketing data to deliver instant insights through BI and AI, helping to eliminate complexity, delivering laser-focused insights to optimize budgets, enhance campaign efficiency and marketing ROI.
As a rapidly scaling Series A startup that raised $34 million in funding, we’re proud to partner with globally recognized brands such as ASUS, Docker, Activision, H&R Block, and many others. Our diverse team of curious, open, and hard-working individuals is committed to cultivating a fast-paced, agile culture that values personal and professional growth.

ABOUT THE ROLE

We’re looking for a hands-on AI Principle Engineer to help us build the future of AI at Improvado. It’s a unique opportunity for someone excited to define the product vision based on real user needs and directly build the systems that power it.
You’ll work closely with our CEO (who’s also our Chief Product Officer with a strong technical background) to drive the direction of our AI features. From high-level strategy to low-level implementation, you’ll bridge product thinking and deep engineering. You’ll also help guide a small, talented team of AI engineers — mentoring, setting direction, and writing production code alongside them.
Note: This is a high-ownership, high-impact role with a 6-day workweek. We’re building fast and need someone ready to move with urgency and focus.

WHAT YOU’LL DO

• Co-own the AI product and technical roadmap with our CEO
• Serve as the link between engineering and the business: ensure the team understands not just what to build, but why it matters
• Design and implement key AI-powered features from the ground up
• Drive technical architecture decisions and ensure high code quality and reliability
• Write scalable, production-ready code and build intelligent agents using modern LLM frameworks
• Define and track success metrics — from model performance to user satisfaction and adoption