AI Product Engineer
About The Position
We're a small team within Blackboard building a new AI-native product. This is not a feature team, and it's not an enhancement to an existing product — we're building something new. AI coding tools — Claude Code, Cursor, and similar — generate the majority of our code. Our engineers are architects, reviewers, and product thinkers: people who know how to direct AI development agents effectively, ship features end-to-end and make judgment calls when the requirements are still evolving. On a team this small, there are no handoffs. The person who designs a feature ships it. The person who builds the data pipeline also builds the product surface that consumes it. We're looking for builders who are energized by that scope, not overwhelmed by it. Everyone on the engineering team owns features end-to-end — from the data pipeline to the AI orchestration layer to the student-facing interface. The work varies by the week, and the surface area is large.
Requirements
- You work natively with AI coding tools (Cursor, Claude Code, or equivalent) — not as a curiosity, but as your actual development workflow
- You've shipped products to real users — full-stack, with real accountability for whether they work
- You're comfortable across the stack: React/Next.js/TypeScript on the frontend, Node.js or Python on the backend, PostgreSQL for data
- You've integrated LLM APIs or built AI-powered features in production (streaming, structured outputs, agent patterns)
- Strong product instincts: you notice when something feels off in the user experience and you fix it, even if it wasn't in your ticket
- You can move fast, make good decisions under ambiguity, and leave the codebase better than you found it
- Fluency in written and spoken English
Nice To Haves
- Experience with event-driven data pipelines, vector stores, or RAG pipeline design
- Familiarity with infrastructure and deployment (CI/CD, managed services, observability tooling)
- Background in consumer-facing products where user behavior and engagement genuinely matter
Responsibilities
- Product Surfaces
- Student-facing product interfaces — the core user experience that learners interact with daily
- Real-time features that surface AI intelligence in ways that feel immediate and useful, not mechanical
- Responsive, performant UI built on a shared component library
- Backend & AI Integration
- API routes and backend services connecting the user interface to the intelligence and data layers
- AI orchestration integration: streaming responses, agent outputs, structured recommendations in context
- Event-driven ingestion pipelines that translate platform activity into structured, reliable signals
- Data & Infrastructure
- Storage architecture and schema design for persistent user state — built to evolve as the product grows
- Deployment infrastructure, CI/CD, and environment management — no dedicated DevOps to hand off to
- Data access controls and audit logging that satisfy institutional security and compliance requirements
- Quality & Velocity
- End-to-end feature ownership from spec to production — including testing, monitoring, and iteration
- Active use of AI-assisted development tooling to maintain high output without sacrificing code quality
- Contribution to shared component libraries and codebase standards as the team grows
- Stay on the bleeding edge of the AI ecosystem — Claude Code, Cursor, OpenAI Agent SDK, MCPs, and whatever comes next. When a better way to build exists, find it and bring it in.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
