Learning Engineer

Job description

**About LearnVector** 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. **About the role** You will apply deep expertise in teaching to build products that result in learners actually developing new skills — not just consuming content. You sit at the intersection of pedagogy and engineering: close enough to the craft of teaching to know what makes an explanation land, and close enough to the technology to ship it. Great teaching is full of judgment calls: when to show a worked example, when to make the learner produce, when to let them struggle, when to step in. Your job is to encode that judgment into products and into the systems that generate learning experiences at scale, so the quality of the best human teaching survives being automated. **What you will do** - Design learning experiences — practice tasks, explanations, feedback, assessments — grounded in how people actually acquire skills, and build them into the product - Translate pedagogy into specifications AI systems can execute: what a good lesson does, what a good feedback message contains, what a learner should produce and how it's judged - Set and hold the quality bar for AI-generated teaching content: review it, define what "good" means mechanically, and build the checks that enforce it without a human in every loop - Prototype quickly with AI tools — you'll build working learning experiences yourself, not hand off requirement docs - Work with learners directly: watch sessions, run pilots, and turn what you observe into the next iteration - Work directly with the founding team, including Andrew, on what good teaching looks like in this product **What you bring** - Real teaching experience — you have taught people a skill (classroom, corporate training, coaching, course creation) and can articulate what worked and why - In-depth knowledge of instructional design and learning-science fundamentals (retrieval practice, worked examples, feedback, cognitive load) and a record of applying them, not just citing them - Hands-on builder: comfortable with modern AI tools and light engineering (Python or JavaScript, prompt design, quick prototypes); you ship things learners touch - Demonstrated ability to create interactive, applied learning experiences — evidence over credentials - Excellent writing; you can author and edit high-quality learning content in English **Nice to haves** - Experience designing for adult professional learners or workplace upskilling - Experience building with LLMs: prompt pipelines, generated content with quality gates, AI feedback on learner work - Background in assessment design — rubrics, performance tasks, mastery measurement - Experience in marketing, business, or another applied professional domain we may teach **What success looks like** In your first 30 days, you will have shipped a learning experience to real learners and instrumented it well enough to know whether it taught anything. In your first 6 months, the product's core learning experiences will carry your fingerprints — and the standards you've encoded will be enforced by systems, so quality holds as content scales beyond what any one person can review.