Backed by Sequoia.
About the role
You will build our software product end to end — the application learners live in, the backend that serves unique AI-enabled learning experiences at scale, and the infrastructure underneath both. You'll make key architecture and implementation decisions early enough that they'll still matter years from now.
What they're looking for
- - AI-native: you default to AI-assisted coding and building automations in everything you do, and you stay current with the newest AI engineering practices because you can't help it
- - 4+ years building and shipping production web applications end to end
- - Strong TypeScript/JavaScript and modern web frameworks (React/Next.js or similar), plus solid backend engineering (Node or Python), API design, and SQL
- - Experience owning production systems: deployment, monitoring, incident response, performance — you've been paged and made the pager quieter
- - Experience integrating LLM APIs into products, including streaming, and an informed view of what makes AI products feel great or terrible
- - Judgment: you can make an architecture call under uncertainty, state your reasoning in a paragraph, and change your mind when evidence arrives
More about this role
For most of history, great teaching has been scarce. A brilliant teacher who knows you well, adapts to how you learn, and patiently stays with you until you get there — almost no one has had that. AI changes what's possible. LearnVector, founded by Andrew Ng, is building a trustworthy AI guide for learning, with a mission to accelerate human development. We're a small, fast-moving team working on-site in Mountain View, California and backed by a $100 million investment from Coursera.
You will build our software product end to end — the application learners live in, the backend that serves unique AI-enabled learning experiences at scale, and the infrastructure underneath both. You'll make key architecture and implementation decisions early enough that they'll still matter years from now.
The product is unusual in a specific way: every learner's experience is different, generated and adapted for them, and delivered through long-running relationships rather than stateless sessions. Serving that well is a real systems problem — state and memory over months, streaming AI interactions that feel instant, content pipelines with verification stages, and the observability to know what...
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