About
We're building the future of storytelling and video editing.
We're a small team that moves fast and builds things we're proud of.
We care obsessively about taste: in design, in product, in every detail.
We're solving these hard problems.
We're backed by a Tier-1 global fund, YC, and founders of billion dollar companies.
Engineering
Cardboard is a video editor you can talk to. Behind it are proxy transcodes, parallel exports, GPU analysis, AI generation, and the credits that pay for it all.
We're hiring a Backend Engineer to own those systems as products, measured by how fast they are, how often they fail, and what they cost.
What you'll own
Exports, media processing, and generation.
Long-running jobs that recover on their own and report honest progress.
Credits and usage, charged fairly and never twice.
Who you are
You've owned production backend systems while the product kept changing.
You think clearly about state, queues, and workers, and you care about cost as much as speed.
You're strong in TypeScript, and comfortable in Python.
Bonus: deep FFmpeg or codec experience.
How we work
You own outcomes, not tickets: decide what to build, ship it, and check it worked.
You talk to users and look at the data.
You build with AI agents every day, and you're the taste and judgment in the loop.
What success looks like
Within your first six months:
Exports and processing are measurably faster and fail less often.
You know what each kind of job costs us, and the biggest costs have come down.
What you get
You'd be surrounded by people who are absurdly good at what they do. One started coding at 11 and shipped an app with 6M+ downloads in high school. One got into CS engineering at 14 and has been working on distributed systems for 8+ years. One's an ex-founder who took a company to 1.2M users and $300M+ in transactions. That's the team. We're looking for someone who'll raise the bar on technical craftsmanship and product quality. Apart from that you'd get:
Competitive salary and founding-team equity.
Unlimited tokens across every AI model. Use whatever you want, as much as you want.
A healthy budget for AI tools and any peripherals you need to do your best work.