Full-Stack Engineer
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- Own a user workflow from browser UI through API and database
- Build secure product flows with tests, integrations and maintainable full-stack architecture
- Operate end-to-end products in production with performance, observability and AI-ready patterns
Benchmark: OpenAI Software Engineer, Full Stack + Anthropic Senior/Staff Full-Stack engineering (current Sep 2026) + Harvard CS50 Web project-based full-stack curriculum Market refresh: product-minded end-to-end ownership across React/TypeScript, backend services, relational data, auth, APIs, testing, cloud deployment, observability, performance, security and increasingly AI-enabled product workflows.
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Own a user workflow from browser UI through API and database
Modern web UI + full-stack application structure
Coverage: HTML/CSS / React-Next.js / TypeScript / Routing / Server Components
Backend APIs + relational data
Coverage: Python APIs / SQL / ORM / Schema / Migrations / Validation
Build secure product flows with tests, integrations and maintainable full-stack architecture
Type-safe product flows, auth + API design
Coverage: TypeScript / API Contracts / Auth / Permissions / Error Handling / UX States
Testing, Git + quality automation
Coverage: Git / Unit-Integration-E2E / Playwright / CI Quality Gates
Operate end-to-end products in production with performance, observability and AI-ready patterns
Deployment, CI/CD + end-to-end reliability
Coverage: Containers / CI/CD / Monitoring / Rollback / Cross-Layer Debugging
Performance, SLOs + production product quality
Coverage: Core Web Vitals / API Latency / SLOs / Cost / Observability / User Impact
Role-Readiness Proof / Capstone
Run a 7-day Full-Stack Product Sprint: build and deploy a responsive TypeScript/React or Next.js app with a real backend API, Postgres-style data model, authentication and permissions; add background work or webhooks; write unit/integration/E2E tests; configure CI/CD, telemetry and SLOs; profile frontend and backend performance; conduct user testing; optionally add one grounded AI feature with an eval; ship a staged release and post-launch readout.
Proof Rubric
- Working end-to-end product
- coherent frontend/backend/data boundaries
- secure authentication/authorization
- correct schema and migrations
- reliable API contracts
- meaningful automated tests
- production deployment
- observable errors/latency
- performance evidence across UI and backend
- staged rollout/rollback
- product judgment and measurable user outcome