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LEARNING PATH · AI Engineering

Land Your First Dev Job in the AI Era

For new and bootcamp grads facing "AI is killing junior roles — what do I even learn?"

Beginner ~3h 45m10 lessons11 steps

The honest, ordered path for a new grad in 2026: understand what actually changed for juniors, learn the fundamentals AND the new core skill (directing and reviewing AI), build a portfolio of judgment, and walk into the 2026 interview ready to work with AI in the room.

  • Understand what AI changed for junior roles — and what it didn’t
  • Pair strong fundamentals with the new skill of directing and reviewing AI
  • Build a portfolio of real, shipped projects that prove judgment
  • Walk into the 2026 interview ready to work with AI in the room
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  1. HandbookNext up

    The New Grad in the AI Era

    Start here — what actually changed for juniors, and what to do about it.

  2. ToolTool · optional

    Agent Time-Horizon Explorer

    See the trend you’re entering — how fast agent capability is growing.

  3. Handbook

    The Agentic Coding Handbook

    Learn to work AI-augmented — the agentic coding workflow.

  4. Handbook

    Vibe Coding vs Spec-Driven Development

    The discipline that keeps you employable: specify and verify, don’t just vibe.

  5. Challenge

    Spot the Bug in AI Code

    Practice the new core skill: reviewing AI code adversarially.

  6. Challenge

    Two Sum

    Keep the fundamentals sharp — understand what you ship, don’t just paste it.

  7. Challenge

    Write a Verifier

    Design a verifier — the mindset behind trusting AI output.

  8. Handbook

    AI-Assisted Interviews

    The 2026 interview: working with AI in the room, not against it.

  9. Handbook

    The Resume & Portfolio Handbook

    Present a portfolio of judgment — real shipped things over credentials.

  10. Handbook

    The Salary Negotiation Handbook

    Anchor your first offer with data, not hope.

  11. Roadmap

    AI Engineer Roadmap

    A concrete direction to grow into once you’re in.

How this path works

This path is for people who just finished a degree or a bootcamp and are reading headlines that say junior roles are disappearing. It is also for anyone in their first year or two who wants a clear picture of what to practice. It assumes you can write basic code in at least one language. It does not assume you have a job yet.

The honest picture is mixed. Work that used to train juniors, such as boilerplate, glue code and first-draft tests, is increasingly produced by tools, and hiring for the generic entry-level role has tightened in many places. What has not gone away is the need for people who understand what the code does, can review a plausible but wrong change, and can explain their reasoning. Interviews are also changing: some now let you use an AI assistant and judge how well you steer and check it.

The order reflects that. You start with the situation, then see the trend you are entering, learn the working style of agentic coding and why specifying and verifying beat blind prompting, and practice review, fundamentals and verification with runnable challenges. Only then do you turn to interviews, the portfolio, the first negotiation and a direction to grow into.

What each step gives you

  1. The New Grad in the AI Era

    A direct answer to "what do I even learn now?" It separates what AI automates in junior work from what it does not, then lays out three layers to build: fundamentals, working with AI through review and specification, and learning how to learn. Five concrete moves and a first-90-days plan give you something to do on Monday.

  2. Agent Time-Horizon Explorer

    Set a current task length and a doubling period, and the tool projects when agents might handle day-long or week-long tasks. Treat the output as a scenario, not a forecast, and change the assumptions to see how sensitive it is. It helps you plan skills around a moving target instead of reacting to each headline.

  3. The Agentic Coding Handbook

    Explains what a coding agent is (a loop with context and tools), why the instruction file is your highest-leverage artifact, and the workflow of planning first and then verifying small steps. It also covers security and team discipline. You will be ready for interview questions about how you use AI tools day to day and where you stop trusting them.

  4. Vibe Coding vs Spec-Driven Development

    Compares prompting and keeping what works with writing a spec, a plan and tasks before generating code. It explains why the first approach is fine for throwaway prototypes but breaks at scale. For a junior, being able to say when you would write a spec first is a strong answer to a common design-process question.

  5. Spot the Bug in AI Code

    An assistant wrote a history trimmer for a chat context window. It looks right, type-checks and passes the obvious case, and your job is to find what it gets wrong and fix it, with hidden tests. This is the reviewing skill in miniature: distrust code that merely looks finished, and probe the boundaries.

  6. Two Sum

    The classic warm-up: find two numbers that sum to a target. A brute-force pair loop is quadratic, and a hash map reduces it to one pass. It is here as a fundamentals check, because reviewing AI output requires you to recognize why one approach beats another and to state the complexity without help.

  7. Write a Verifier

    You write a function that checks an agent's submission against ground truth and cannot be fooled by a self-reported score or missing tasks. It teaches the mindset behind trusting generated work: recompute, demand full coverage, and compare with types intact. It prepares you for questions about how you would know an AI-written change is correct.

  8. AI-Assisted Interviews

    Describes the format in which an assistant is allowed in the room and what is measured instead: how you decompose a task, direct the tool, and review what comes back. It lists the new round types and how to rehearse them. Practice here prevents the common failure of accepting generated code without reading it.

  9. The Resume & Portfolio Handbook

    Covers the two readers of your resume, an applicant tracking system that scores keyword coverage and a human who skims, with a quick score-your-own-resume exercise. For a new grad it focuses on the portfolio: a few real, finished projects with a clear problem and decisions you can defend. Use it to rewrite your resume once, with evidence.

  10. The Salary Negotiation Handbook

    Lays out why a polite counter is usually low risk, how a competing alternative gives you leverage, and how a first-salary gap compounds over years. It includes scripts and common mistakes. Read it before your first offer arrives so you have a researched range and a calm reply ready, rather than improvising under pressure.

  11. AI Engineer Roadmap

    An 18-station map in three tracks, from Python and how LLMs work through retrieval, agents and fine-tuning to evaluation, guardrails and serving. You do not need it all now. Pick the first track as a growth direction so your early projects point somewhere, and return to later stations as your first role exposes real problems.

A study plan

  1. Days 1–7

    Read The New Grad in the AI Era and explore the time-horizon tool. Write down your three layers: which fundamentals are weak, which AI habits you use, and what you want to learn. Pick one project idea that solves a problem you personally have.

  2. Weeks 2–3

    Read Agentic Coding and Vibe Coding vs Spec-Driven Development, then start your project using a written spec. Solve Spot the Bug in AI Code and Two Sum without help, and keep a log of every bug an assistant introduced in your project.

  3. Week 4

    Do Write a Verifier and add a test or check to your project that would have caught the bugs from your log. Read The AI-Assisted Interviews Handbook and rehearse one practice session where you narrate how you direct and review the tool.

  4. Weeks 5–6

    Finish the project and publish it with a short write-up of decisions and mistakes. Work through the Resume and Portfolio Handbook, rewrite your resume around that project, and skim the Salary Negotiation Handbook so the research is ready before any offer.

  5. Ongoing

    Open the AI Engineer Roadmap and choose one station per month to study through a small build. Keep applying and keep a short list of what each interview asked, then use that list to decide the next thing to practice.

Questions about this path

Is AI really killing junior developer jobs?

AI has changed the work that used to train juniors, and hiring for the generic entry-level role is tighter in many places, so the worry is not imaginary. But the need for people who understand systems, review code critically and take responsibility for what ships has not disappeared. What has changed is the bar for showing it. A few finished projects, clear reasoning about trade-offs and comfort working with assistants carry more weight now than a list of courses. Treat the market as harder, not closed.

What should a new grad learn first in 2026?

Fundamentals first: one language well, data structures, how the web and databases work, and the habit of testing. Then add the working style: writing a short spec, directing an assistant in small verified steps, and reviewing its output as if a stranger wrote it. Finally, build something real that you can explain end to end. Learning a new framework matters less than being able to read, debug and defend code you did not write yourself.

Can I use AI to write code in a job interview?

It depends on the company, so ask the recruiter in advance. A growing number of interviews allow an assistant and then evaluate how you break down the task, prompt it, and catch its mistakes. Others still ban it entirely. Prepare for both: be able to solve a problem alone, and practice a session where you narrate your decisions while using a tool. Either way, make sure you can explain every line you submit, because that is what the interviewer is trying to learn.

What projects should a new grad put in a portfolio?

Choose two or three projects that solve a problem someone actually has, ideally including you. Finished beats ambitious: it should run, have a short README, tests for the risky parts, and a note on what you would change. Include one project where you used an AI assistant and write down where it was wrong and how you caught it. Reviewers learn more from your decisions and corrections than from a long list of technologies.

The scariest question for a new or bootcamp grad in 2026 is “AI is killing junior roles — what do I even learn?” The honest answer: the generic entry rung genuinely got tighter, but the path that works now is clearer, not hopeless. This path orders it so you’re not guessing.

You start with what actually changed for juniors (and what didn’t), then build the new core skill on top of fundamentals: directing AI, and — more importantly — reviewing it adversarially, because the grad who understands and can debug what they ship is the one who lasts. You finish on the job hunt itself: the 2026 interview with AI in the room, a portfolio of judgment over credentials, and a data-anchored first-offer negotiation. Demonstrated ability is the currency now, and this is the ordered way to build it.

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