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AI Fund

AI Engineer

Mountain View, CAfull timemidAdded today

About this role

LearnVector, founded by Andrew Ng, is seeking an AI Engineer to build core agentic systems for a personalized AI tutoring platform. You'll design and implement sophisticated systems that understand learners, plan adaptive learning paths, and deliver verified teaching—moving well beyond simple API wrappers to solve hard problems in long-term learner modeling, quality evaluation, and content verification.

What you'll do

  • Design and build agentic tutoring loops with multi-step reasoning, tool use, memory, and long-horizon planning
  • Develop the learner model—an evolving, evidence-backed representation of what each learner knows, wants, and responds to
  • Create evaluation harnesses for conversational and teaching quality; operationalize what 'learning happened' and measure it
  • Implement guardrails and verification systems to ensure generated content meets quality and accuracy standards
  • Own systems end-to-end from design through implementation, evaluation, and iteration using real learner data
  • Collaborate with the founding team on core product questions about AI tutoring efficacy and learner outcomes

What they're looking for

  • LLM API integration and agentic workflows (Claude, OpenAI, or similar)
  • Production LLM systems design, deployment, and evaluation
  • Python and/or TypeScript/Node.js backend engineering
  • Long-context memory patterns and structured output handling
  • Conversational AI design and quality evaluation frameworks
  • Learner modeling, personalization, or recommendation systems
  • Metrics definition and data-driven iteration
  • AI-native coding practices and experimentation
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AI Fund

AI Fund builds and invests in AI-powered applications spanning healthcare, productivity, manufacturing, and developer tools. The company is hiring AI Engineers, ML specialists, and full-stack engineers to deploy production machine learning solutions, develop AI-driven platforms for chronic care and business intelligence, and contribute to open-source AI infrastructure projects.

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Likely interview questions

  • Walk us through a production LLM system you've shipped—what were the hardest evaluation or quality problems you solved?
  • How would you measure whether a tutoring conversation actually taught something, and what would you build to track it over weeks?
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