Backed by Sequoia.
About the role
The AI Engineer will build the agentic systems at the core of the product: systems that understand each learner, plan a path with them toward skills worth having, and work with them step by step until they get there.
What they're looking for
- - AI-native: you default to AI-assisted coding and building agentic automations in everything you do, you have an appetite for and record of experimenting with the newest AI engineering practices
- - Experience as a software engineer, with substantial hands-on work building with LLM APIs (Claude, OpenAI, or similar): agentic workflows, tool use, structured output, long-context and memory patterns
- - Experience shipping and operating LLM systems in production, including evaluating them - you have opinions about evals because you've built them
- - Strong Python and/or TypeScript/Node engineering skills, comfort owning services end to end
- - Ability to turn a fuzzy product question ("is the tutor actually helping?") into a measurable system, and ship without heavy oversight
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.
The AI Engineer will build the agentic systems at the core of the product: systems that understand each learner, plan a path with them toward skills worth having, and work with them step by step until they get there.
This is not a wrap-an-API role. The hard problems are the ones frontier models don't solve on their own: maintaining an accurate picture of a learner over weeks and months, deciding what to teach next and when to hold back, keeping long-running conversations useful rather than merely pleasant, and verifying that generated teaching is correct before a learner ever sees it. You'll own systems end to end — design, implementation, evaluation, and iteration against real learner data.
- Design...
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