Residency programs for builders

Learn by building. Ship something real.

Structured programs where you build production-grade projects, get continuous AI feedback, and graduate with the skills and portfolio that get you hired.

Explore Programs

How it works

Gain work-level skills through real projects

Every residency follows the same principle: learn from real-world examples, build your own version, get continuous AI feedback, and ship something you can show.

Learn from the real thing

Start from production examples — not tutorials. Study how real systems, campaigns, and products are built, then apply the same patterns yourself.

AI reviews your work

Get structured feedback within minutes, not days. AI catches gaps, asks the right questions, and keeps you moving. Experts step in when it matters.

Graduate with proof

Every residency ends with a live deliverable — a deployed product, a real campaign, a working system. Something you can point to, not just talk about.

Outcomes

Graduate with proof, not promises

You don't get a PDF. You get a live credential — a URL employers can open and interrogate.

m
Live Credential
Verified
m

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.

VerifiableEvery claim links to a real artifact. Nothing self-reported.

PermanentThe credential stays live as long as your deployed app runs.

HonestGaps 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.

Available via MCP in
ChatGPT
Claude
Employer interviewtrymoss.ai/c/alex-chen-ai
Can this candidate design a RAG pipeline from scratch?
Yes — Stage 2 includes a multi-tenant Supabase pgvector schema with a documented chunking strategy tradeoff, defended in a live design review. Architecture diagram in PR #2.
We're hiring for a mid-level AI engineer. How does Alex fit?
Strong fit. Top 5% eval score, 6 reviewed PRs with tradeoff reasoning, 3 design interviews passed. Gap: minimal frontend experience — deployed UI is functional but bare.