How to Interview Junior Developers in the AI Era
Traditional coding interviews are useless when AI writes the code. Here's a practical guide to evaluate what actually matters: code reading, critical evaluation of AI output, and systematic debugging.
“AI will replace junior developers.” The narrative floats around tech like a prophecy.
I disagree.
High-functioning teams still depend on a healthy distribution of seniority. Juniors aren’t disappearing — but the job is changing faster than interview processes are. Most companies are still giving LeetCode puzzles while Claude and Cursor generate full features in one prompt.
If a junior can paste your challenge into an AI tool and get a working solution, what exactly are you testing?
Needing to hire developers with little experience, I had to rethink everything. The answer isn’t banning AI or inventing “AI-proof” riddles. The answer is understanding what juniors actually do now, and deriving interview requirements from real workflows rather than tradition.
What Juniors Actually Do (With AI)
When a junior gets assigned a feature today, their real workflow looks like this:
Understand product requirements and fill in the edge cases the PM missed
Understand existing architecture and how the feature fits into it
Use AI to draft technical design options and estimate time
Use AI tools to generate the code
Critically evaluate what AI produced — the accountability is still theirs
Think through edge cases (API failures, null states, limits, memory issues)
Debug flows when something breaks
Move code through deployment
Understand the full path to production
Some steps can be taught on the job. But several fundamentals cannot be built quickly. These are the skills we must test in interviews.
What Can’t Be Taught Quickly
Three core capabilities are non-negotiable:
1. Understanding existing architecture
Reading unfamiliar code, forming mental models of the system, understanding databases, APIs, queues, caching, and the shape of data flows.
2. Critical evaluation of AI output
AI produces working-but-flawed code constantly. Juniors must be able to spot poor complexity, hidden loops, redundant operations, unsafe assumptions, missing validations, and mis-architected logic.
3. Systematic debugging
When something breaks — and AI will cause bugs — can they form hypotheses, use logs, follow traces, and reason step by step? Or do they thrash around changing random lines?
These rely on the bedrock fundamentals you cannot install quickly: CS basics, code reading fluency, and structured thinking.
Two Additional Capabilities Now Matter More Than Ever
Learning velocity
AI accelerates development loops. Juniors who learn quickly from AI explanations, follow-up questions, refactoring suggestions, and failure patterns become exponentially better. Those who don’t become exponentially stuck.
This needs to be visible during the interview.
Communication as an engineering skill
A junior today writes more debugging narratives, AI prompts, clarifications, and design notes than code.
If they cannot explain their thinking, they cannot engineer — especially in an AI-mediated environment.
The Interview That Actually Works in 2025
Stage 1: Code Review Exercise (45 minutes)
Give them AI-generated code that “works” but contains obvious issues. Ask:
“An AI tool generated this for a feature. Review it as if a teammate asked for feedback.”
Example:
def search_users(query, all_users):
results = []
for user in all_users:
if query.lower() in user[’name’].lower() or query.lower() in user[’email’].lower():
results.append(user)
# Sort by relevance
sorted_results = []
for result in results:
score = 0
for char in query:
if char in result[’name’]:
score += 1
for sorted_result in sorted_results:
if score > sorted_result[’score’]:
sorted_results.insert(0, {’user’: result, ‘score’: score})
break
else:
sorted_results.append({’user’: result, ‘score’: score})
return [item[’user’] for item in sorted_results]
The goal is not for them to fix it, but to reason about it.
What to look for:
Do they spot inefficiencies (multiple nested loops, unnecessary sorting logic, no pagination)?
Do they mention missing edge cases (empty query, missing fields, large datasets)?
Can they articulate specific improvements instead of vague sentiments?
Do they ask clarifying questions? That shows communication and learning velocity.
Good answers sound like:
“This scales poorly because we search every user for every query. We should index, paginate, and rely on built-in sorting instead of a manual insertion sort.”
Red flags:
No mention of performance at all
Cannot verbalize edge cases
Suggestions are abstract (“this looks bad”)
No questions asked
Stage 2: Debugging + System Understanding (45 minutes)
Example 1: E-commerce Checkout Bug
Walk them through the system:
Frontend → API → Payment Service → Database
Show logs:
14:23:01 - Received checkout request
14:23:01 - Validating 1 items
14:23:01 - Total calculated: 100
14:23:01 - Calling payment service
14:23:02 - Payment service error: insufficient_funds
14:23:02 - Rolling back transaction
14:23:02 - ERROR: Failed to save order - Database connection timeout
Watch their process.
Do they see:
Two separate issues?
The order in which things fail?
The likely cause vs. the actual symptom?
The missing edge cases around retries, partial failures, and async work?
Then ask:
“We need to add an email confirmation when orders complete. Where does it fit, and what can go wrong?”
Good answers include queues, retries, idempotency, and separation between order success and email success.
Example 2: User Authentication Flow
Correct password. Login still fails.
Logs show:
Password validation: SUCCESS
Token generation: SUCCESS
Token validation failed: Invalid signature
A strong junior immediately investigates:
environment mismatch
rotated or misconfigured JWT secret
multiple services using inconsistent keys
Then ask them to walk through a password-reset flow and failure modes.
The Skills You Are Actually Measuring
A strong junior in 2025:
Reads code fluently and sees multiple categories of issues
Evaluates AI output instead of accepting it blindly
Debugs with hypotheses, not hope
Understands system flows, not just isolated functions
Communicates clearly
Shows fast learning loops with guidance or AI explanations
A no-hire:
Can’t spot obvious complexity problems
Debugs by random changes
Has no mental model of system structure
Cannot reason out loud
Treats AI output as ground truth
The Real Shift
Hiring juniors for their ability to write code is obsolete.
We now hire them for their ability to think about code.
AI expands their capability — if they have the fundamentals to judge, correct, and debug what the AI produces. Without that, AI becomes a liability.
The fundamentals matter more than ever, not less.
Because when AI is writing your code, the only safeguard between your product and absolute garbage… is whether your junior developer actually understands what they’re looking at.
This is gold! Do you just have example GitHub repos for those debugging questions or do you use something like a branch of your actual product repo?