AI Engineer Residency
Build a production LangGraph agent from scratch in 6 weeks. Study a real architecture, replicate it for your own domain, ship it live. AI reviews your code. Mentors step in when it matters.
Apply to Join
In partnership with Moonstone Labs — top performers get invited to work on real client projects
Your workspace during the residency
What you build
Your own AI agent — from zero to deployed
You'll replicate the architecture of a production agent service, then rebuild it for a domain you choose: StudentBuddy, JobBuddy, FitnessBuddy, or your own idea.
Agent graph architecture
START → initialize → router → generate_reply → generate_decision
↑ ↓
└──────── memory_updater ───────┘
↓
implementation → ENDLangGraph
Agent orchestration
FastAPI
Streaming API
Supabase
Persistence
LangSmith
Observability
The program
5 stages to production
Clarify requirements
Define your agent's domain, sections, and acceptance criteria. Write a product spec with user stories and edge cases. MOSS reviews your doc, then conducts a live video interview to pressure-test your thinking before you write a line of code.
Design & plan
Produce an architecture diagram, LangGraph state schema, and Supabase table definitions. MOSS challenges you on one key design decision — caching strategy, memory model, or routing logic — in a second video review before sign-off.
Build
6 pull requests, one node at a time: initialize → router → generate_reply → generate_decision → memory_updater → implementation. Every PR must include a LangSmith trace and a written tradeoff note. MOSS reviews each PR in Slack within minutes.
Test
Build a golden dataset of 20+ test cases covering happy path, edge cases, and adversarial inputs in LangSmith. Run evals until your score hits ≥ 0.8 across all sections. MOSS runs a final adversarial sweep before sign-off.
Deploy & monitor
Ship backend to Railway, frontend to Vercel. Stand up a LangSmith monitoring dashboard with live traces, latency metrics, and error rates. Once metrics are stable for 48 hours, a mentor joins a final video call to sign off. You graduated.
Why this program exists
Built for how AI teams actually hire
Designed with Moonstone Labs, an AI studio that ships production agent systems for growth-stage companies. The curriculum mirrors real client work — and strong graduates get invited to interview for paid projects.
Real-time video design reviews
Talk through your architecture live with an AI interviewer. No slides, no docs — just a conversation, like a real engineering team.
Expert-level code review
Every PR gets structured AI feedback: what's good, what needs work, and the tradeoff questions a senior engineer would ask.
Industry best practices built in
PR discipline, LangSmith traces, tradeoff reasoning, production monitoring. The habits that get you hired.
Prerequisites
What you need before starting
What you graduate with
Graduate with proof, not promises
You don't get a PDF. You get a live credential — a URL employers can open and interrogate.
This certifies that
Alex Chen
completed the AI Engineer Residency
Top 5%
Eval score
6 / 6
PRs merged
5 wks
Completion
Verify or interview this candidate
trymoss.ai/c/alex-chen-ai
Not a PDF. A live credential.
Every graduate gets a unique URL tied to their actual work — PRs, eval scores, LangSmith traces, and design reviews. Share it in a job application or on a resume. Employers get the real picture, not a summary.
Verifiable — Every claim links to a real artifact. Nothing self-reported.
Permanent — The credential stays live as long as your deployed app runs.
Honest — Gaps are included. Employers trust it because it isn't marketing.
Employers can interview the credential.
MOSS connects to ChatGPT and Claude via MCP. Scan the QR code and the candidate's full work record loads directly into the employer's AI tool — ready to answer any question about skills, decisions, or position fit.
“We designed this curriculum the same way we scope client work — start from a real system, understand why it's built that way, then rebuild it. That process produces engineers who think, not just engineers who prompt. Those are the people we want on hard problems.”
Top performers are invited to interview for paid client projects at Moonstone Labs — working on real production systems alongside the team that designed this curriculum.
Apply
Apply to Join
Applications reviewed within 2 business days.