Software Engineer, Native Learning Experiences

We’re looking for a full-stack engineer to define and build a new class of learning experiences. This is an early-stage product area where technical judgment, product sense, and learner empathy are critical. You will be setting a technical vision for how people use AI to learn how to use AI, safely and beneficially.

This is a hands-on, 0-1 product engineering role with broad technical and product ownership. You’ll set direction, make foundational decisions, and ship the first versions of experiences that can grow into the default way people learn at work. You will drive full-stack product experiences end to end, from prototype through launch, instrumentation, iteration, and production hardening. The work spans interaction design, frontend implementation, backend APIs and services, learner state, content and runtime integration, telemetry, evaluation, reliability, safety, accessibility, and launch readiness.

You’ll work closely with our education, GTM, and engineering teams to translate how people learn into products people want to use. bring role- and skill-based learning paths into the product, designing coaching, feedback, and adaptive support which responds to each learner’s goals, context, and progress. You’ll lead focused experiments, measure what helps learners progress and where they struggle, and use that evidence to shape what comes next.

Strong candidates will be product engineers with experience and a strong interest in education, learning science, assessment, behavior change, or AI literacy. You’ll help shape how OpenAI supports structured, adaptive learning in the flow of work, from a learner’s first entry point through practice, feedback, progress, and repeated use.

In this role, you will:

Own the vision and execution for OAI’s native learning offering

Build and launch end-to-end product experiences that help users build and apply AI skills through real work, starting in ChatGPT.

Design the core systems behind those experiences, including learner state, progress and re-entry, content and runtime integration, experimentation, telemetry, and evaluation

Create reusable components and internal tools that allow education and content partners to develop, configure, test, and improve learning experiences

Build and test clear entry points, realistic hands-on practice, useful feedback, telemetry, and evaluation. Use evidence from real use to decide which capabilities for saved context, progress, re-entry, and reuse are needed next.

Partner closely with colleagues across Customer Readiness, EDU Engineering, learning science, research, design, data, content, support, and customer-facing teams. Reuse technical and learning patterns where they help while keeping the experience grounded in professional and enterprise use cases.

Work closely with design and users to turn product and learning goals into clear requirements, prototypes, milestones, reusable UI patterns, and product metrics, then refine the experience through evidence.

Define the learner intent and problem for each release, then use product data, learner feedback, and operational signals to evaluate product quality, learning, transfer to real work, and adoption against that intent.

Make pragmatic tradeoffs across speed, quality, scalability, and operational simplicity in a 0-to-1 product area.