Software Engineer, Native Learning Experiences

OpenAI•San Francisco, CA

4d•$266,000 - $385,000

About The Position

We are seeking 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.

Requirements

Substantial experience building and operating high-quality user-facing products with a record of staff-level technical leadership and hands-on delivery
Strong full-stack engineering skills, including modern frontend technologies such as TypeScript and React, backend services and APIs, relational databases, stateful user flows, and instrumentation, with practical judgment about distributed systems, reliability, and performance.
Experience building in 0-to-1 or fast-moving product environments where user needs, product shape, and success measures are still evolving.
Strong user empathy and a high bar for product and interaction quality, accessibility, reliability, safety, security, and performance.
Experience collaborating across engineering, research, product, design, data, content, operations, and customer-facing teams.
Ability to use qualitative feedback and product data to identify friction, prioritize work, and improve outcomes.
Clear written and verbal communication, including the ability to explain technical decisions and tradeoffs to different audiences.
Careful judgment when building products that shape how people understand and use AI.

Nice To Haves

5+ years of professional engineering experience (excluding internships) in relevant roles at tech and product-driven companies
Former founder, or early engineer at a startup who has built a product from scratch is a plus
Experience building education, edtech, learning, tutoring, coaching, assessment, onboarding, training, or enablement products.
Experience in AI literacy, workforce learning, enterprise enablement, developer education, or similar spaces where users need to build confidence and skill over time.
Experience building AI products, LLM applications, developer tools, agent-assisted workflows, or personalized user experiences.
Experience building customer-facing, revenue, sales, support, or go-to-market systems where product quality and operational feedback loops matter.
Experience translating research into product decisions, experiments, evaluation, measurement, and iteration.
Experience building product surfaces used by both individual users and organizations, including admin, reporting, or support-readiness considerations.

Responsibilities

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.