Burnt isn't building software on top of ERPs. We don't believe ERPs will exist in the long run. They were built for a world where humans key in data and software stores it. That world is ending. We're building the Operating Brain for the global supply chain. Backed by Y Combinator.
About the role
This is a Member of Technical Staff position for a full stack engineer who lives at the intersection of product engineering and applied AI. You will own features from the database to the browser, and you will ship AI agents that run in production against real traffic and real money. This is not a research seat and it is not a prototype factory. We ship. You will be expected to architect systems, debug them when they break at 2am, and make them better the next morning.
What they're looking for
- Design, build, and ship full-stack features end to end, from data model to UI, that go live and stay live
- Build production-grade AI agents and the eval frameworks that prove they work
- Stand up feedback loops and self-learning systems so the product gets sharper the more it runs
- Treat observability as a first-class concern: logging, tracing, and alerting baked in from day one, not bolted on later
- Own production incidents end to end, from the page to the post-mortem to the fix that stops it recurring
- Handle large-scale datasets at the application layer without the system falling over
More about this role
This is a Member of Technical Staff position for a full stack engineer who lives at the intersection of product engineering and applied AI. You will own features from the database to the browser, and you will ship AI agents that run in production against real traffic and real money.
This is not a research seat and it is not a prototype factory. We ship. You will be expected to architect systems, debug them when they break at 2am, and make them better the next morning.
- Design, build, and ship full-stack features end to end, from data model to UI, that go live and stay live.
- Build production-grade AI agents and the eval frameworks that prove they work.
- Stand up feedback loops and self-learning systems so the product gets sharper the more it runs.
- Treat observability as a first-class concern: logging, tracing, and alerting baked in from day one, not bolted on later.
- Own production incidents end to end, from the page to the post-mortem to the fix that stops it recurring.
- Handle large-scale datasets at the application layer without the system falling over.
- Use AI coding tools to multiply your output, while holding the bar on what good code actually looks like.
You should...
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