One of six curricula in the AI Career Curriculum
AI Workforce
You work alongside the model: you direct it, check its output, and redesign your own work around it.
5 role tracks, new and being written now. Free and MIT-licensed.
Start with AI-Assisted Software EngineerEvery repo on GitHub
- AI WorkforceWork alongside the models
- AI StartupTurn it into a company
- AI GovernanceGovern, secure, and assure AI
- AI EngineeringBuild with the models
- ML EngineeringBuild and train the models
- AI InfrastructureRun the platforms models live on
Your relationship to the model
To you the model is a colleague with uneven skills: fast, tireless, often right, sometimes confidently wrong. You don't build or run it. You decide what to hand it, how to brief it, how to verify what comes back, and which parts of the job stay yours. Your craft is the judgment around the model, not the model itself.
This org covers AI in everyday professional work: working alongside the models. It's for engineers, analysts, product managers, and managers whose job isn't building AI but whose work now runs through it: delegating to coding agents and assistants, briefing them well, verifying what comes back, and building team habits that make AI-assisted work fast and safe.
What you own
- Which tasks to delegate to models and agents, and which to keep
- Briefing: the context, constraints, and examples that get usable output
- Verification: reviewing, testing, and fact-checking what the model produces
- Workflows and team habits that make AI-assisted work repeatable and safe
- Accountability for the result, whoever (or whatever) drafted it
What you don't own: The model, the tools built on it, or the policy that governs them. You're their most demanding user: you report where they fail real work and push for the fixes.
The tracks
Ordered by level. Each one opens with Module 00, which maps the role's place relative to the model before the technical work starts.
Practitioner (L20)
Delegating to coding agents, writing specs & context, reviewing AI-written code, testing as verification, repo harnesses
AI-assisted analysis & SQL, checking generated numbers, notebook agents, reproducibility, communicating uncertainty
Product & Delivery (L30)
AI in discovery & specs, prototyping with agents, judging AI output quality, PRDs as context, shipping faster safely
Leadership (L40+)
Redesigning team workflows, review norms for AI-made work, measuring real productivity, skills & career growth
Org-wide AI adoption: tool selection, usage policy, training programs, champions networks, measuring impact
Who you work with
No curriculum owns the model end to end. These are the handoffs with each of the others.
AI Startup
Turn it into a company
- You hand them
- Evidence of where AI changes the economics of a job, which is where the product and hiring bets are
- They hand you
- Decisions about which AI tools the company adopts and how roles change, plus the training and time to adapt
AI Governance
Govern, secure, and assure AI
- You hand them
- How AI is actually being used on the ground, including the shadow uses policy hasn't caught up with
- They hand you
- Clear rules on what may be shared with which tools, and which uses need review, written so people can follow them
AI Engineering
Build with the models
- You hand them
- Workflow requirements and failure reports from daily use, which tell builders what to fix and what to build next
- They hand you
- Tools, agents, and integrations that fit how people actually work, with sensible defaults and guardrails
ML Engineering
Build and train the models
- You hand them
- Concrete examples of where model quality falls short on real tasks, the raw material for better evals and training data
- They hand you
- Models that are better at real work, plus honest documentation of what they're bad at
AI Infrastructure
Run the platforms models live on
- You hand them
- Real usage patterns and pain points (latency, limits, outages) from people whose day depends on the tools
- They hand you
- Reliable, fast, well-documented AI tools with clear limits, so a working day doesn't stall on an outage or a rate limit
What you'll work with
- Coding agents
- Claude Code, Codex CLI, Cursor, GitHub Copilot
- Assistants
- Claude, ChatGPT, Gemini, Microsoft Copilot
- Analysis
- Jupyter, SQL, spreadsheets with AI assistance
- Context & tooling
- CLAUDE.md / AGENTS.md, Model Context Protocol (MCP), skills & hooks
- Governance
- Acceptable-use policies, NIST AI RMF, data-handling rules
How the tracks work
Learn
Each track is a sequence of modules: lecture chapters, hands-on exercises, quizzes, and projects. Start with Module 00 and work forward.
Check your work
The paired solutions repo has a reference walkthrough for every exercise. Attempt each one first; the gap between your answer and the reference is where the learning happens.
Built in the open
The content is written with AI assistance and under ongoing human review, so it can contain mistakes. Report anything wrong through the repo's Issues; fixes ship continuously.