Product Engineer, Fullstack

Cardboard is an agentic video editor for growth/marketing teams and serious creators who need to ship videos consistently. Instead of agencies, long timelines, and infinite review cycles, teams give cardboard a footage + a goal (“3 variants”, “30s hook”, “testimonials”) to get a strong first cut in minutes on which they can iterate quickly.

Cardboard believes AI video creation should make creators faster without taking the creative control out of their hands.

Its editor brings generation, editing, and export into one place, and learns how each creator works along the way.

As Product Engineer, Fullstack at Cardboard, you will own product features from idea through measured outcome.

You will study users and activation data, choose what to build, and implement it across the stack. The role requires product taste, sound reasoning, and comfort using agentic coding tools.

You will work closely with the co-founder, product team, and design.

• Features from idea to outcome. Find the problem, choose the response, ship it, and check whether it worked.
• Context-aware agent features. Connect editor state, assets, timelines, history, presets, and external media.
• Video quality verification. Build checks for technical quality, brand alignment, pacing, and output quality.
• Editing history and versions. Shape branching and merging around concepts that make sense to video editors.
• The full implementation path. Build across frontend and backend without waiting for a handed-off specification.
• Weekly product learning. Use conversations and product data to choose the next change and measure its effect.

• Built a zero-to-one product. You turned an unclear user problem into a working product or feature.
• Owned features across the stack. You made frontend and backend decisions and carried the work into production.
• Made calls from product evidence. You used user conversations or behavior data to change what you built.
• Used coding agents with judgment. You checked generated work, understood its logic, and corrected weak decisions.
• Shipped on short cycles. You reduced scope, released useful work, and learned from production each week.
• Worked as an individual contributor. You owned meaningful product scope while remaining close to implementation.

• Own the outcome. Choose what to build, ship it, and check whether it changed the user experience.
• Talk to users directly. Use their workflow and feedback to sharpen product decisions.
• Read the product data. Find where activation breaks and use the signal to set priorities.
• Ship every week. Cut scope into useful releases and learn from production quickly.
• Use coding agents thoughtfully. Move faster with AI tools while checking every important technical decision.
• Work across functions. Build with design and product through frequent in-person discussion.
• Care about the output. Judge success by the quality of the finished video and the outcome it creates.

Base pay is ₹60 lakh a year, with 0.1% to 0.5% equity through founding ESOPs. You will work with the co-founder and product team in Bengaluru. The role gives you end-to-end ownership of intelligent features in an AI video editor.