Why Your Coding Interview Process is Broken (And You Know It)

Let me tell you about the last time I implemented a binary search tree from scratch at work. It was never. The time before that? Also never. The time I had to reverse a linked list in production? You guessed it—never happened, and if it did, I’d probably be fired for not using the standard library. Yet here we are in 2025, still asking candidates to perform algorithmic gymnastics that have about as much relevance to their daily work as a medieval jousting tournament has to modern transportation. We’ve built an entire industry around preparing for interviews that test skills you’ll never use, while ignoring the skills you’ll use every single day. The coding interview process isn’t just broken—it’s shattered into pieces, and those pieces are drifting further apart by the day. Let me show you why, and more importantly, what we can actually do about it.

The Great Schism of 2025

Something fascinating happened in the tech interview landscape recently. After nearly two decades of relative stability, we’re witnessing what I like to call “The Great Interview Schism.” On one side, you have Big Tech clinging to their LeetCode-style algorithmic challenges like a drowning person clutches a life preserver. On the other, startups and forward-thinking companies are pioneering practical, project-based assessments that actually resemble real work. This isn’t just a minor variation in interview style—it’s a fundamentally different philosophy about what we’re even trying to measure. And spoiler alert: both approaches are broken in their own special ways.

The Big Tech Stockholm Syndrome

Here’s the dirty little secret that nobody in FAANG wants to admit out loud: the algorithmic interview is a game, and everyone knows the rules are arbitrary. It’s like we’ve collectively agreed to pretend that being able to implement Dijkstra’s algorithm on a whiteboard in 45 minutes is somehow predictive of whether you can build a scalable microservice or debug a race condition in production. But why does this charade persist? The answer is frustratingly simple: inertia, scale, and the absence of something provably better. Big Tech companies have invested years calibrating their processes. They have mountains of data correlating interview performance with job performance (even if that correlation is weaker than they’d like to admit). They’ve trained thousands of interviewers. They’ve built entire recruiting machines around this model. Changing it would be like trying to turn an aircraft carrier with a canoe paddle.

The Uncomfortable Truth

Let me share something that might make some people uncomfortable: these interviews kind of work. Not perfectly, not even well by many standards, but they work just well enough. They screen for:

• Raw intelligence and problem-solving ability
• Coding fundamentals (can you actually write code?)
• Tenacity (did you put in the hours to prepare?)
• Communication skills (can you explain your thinking?) Engineering leaders will readily tell you about the nightmare scenarios—the engineers who couldn’t code their way out of a paper bag, the ones who had impressive resumes but couldn’t implement basic logic. The LeetCode gauntlet catches most of these cases. It’s brutal, it’s disconnected from reality, but it’s a filter that provides signal. The problem? It also filters out plenty of talented engineers who either can’t or won’t play the game.

The AI-Shaped Elephant in the Virtual Room

Now let’s talk about the chaos agent nobody saw coming: AI-assisted interview fraud. Picture this: You’re conducting a remote interview. The candidate’s camera is on, they’re nodding along, typing confidently. What you don’t see is ChatGPT running in a hidden window, feeding them solutions in real-time. Or worse—an entire team of engineers in their Discord, solving the problem together while your candidate takes the credit. One Amazon interviewer reported that 4 out of his last 7 junior-to-mid-level candidates were provably cheating with AI tools. Not “we suspect”—provably. That’s a 57% fraud rate. Let that sink in. This has created an impossible trilemma for companies:

1. Increase monitoring → Create a hostile environment that scares away legitimate candidates
2. Make problems harder and more obscure → Further divorce interviews from actual work
3. Accept that AI is now part of the toolkit → Face the uncertainty of what you’re actually evaluating Big Tech chose options 1 and 2, hence the return to mandatory onsite interviews. It’s 2025, and we’re going backwards in time because we couldn’t figure out how to make remote interviews cheat-proof. Meanwhile, companies like Shopify are taking option 3, embracing AI as part of the modern engineering workflow and adapting their processes accordingly. Who’s right? Nobody knows yet, but it’s going to be fascinating to watch this play out.

Let Me Show You What’s Wrong (With Code)

Let’s contrast what Big Tech tests versus what you actually do at work. Here’s a typical interview problem: The Interview Problem:

def find_kth_largest(nums: list[int], k: int) -> int:
    """
    Find the kth largest element in an unsorted array.
    Expected: Implement using a min-heap or quickselect.
    Time complexity: O(n log k) or O(n) average case
    """
    # Candidate sweats profusely, trying to remember
    # if they should use a max-heap or min-heap
    import heapq
    # Wait, is it heapq.nlargest or heapq.nsmallest?
    # Does heapq maintain a min-heap or max-heap by default?
    # PANIC INTENSIFIES
    heap = []
    for num in nums:
        heapq.heappush(heap, num)
        if len(heap) > k:
            heapq.heappop(heap)
    return heap
# Interviewer nods approvingly
# Candidate will never use this code again

The Actual Work Problem:

from statistics import median
from typing import Optional
import logging
logger = logging.getLogger(__name__)
def analyze_user_engagement_metrics(
    user_ids: list[int],
    time_range: tuple[datetime, datetime]
) -> Optional[dict]:
    """
    Pull user engagement data, calculate key metrics,
    handle edge cases, log appropriately, and return
    structured results.
    Real problems: API rate limits, missing data, timezone
    conversions, efficient database queries, monitoring,
    and not breaking production.
    """
    try:
        # Query with proper indexing and pagination
        raw_data = fetch_engagement_data(
            user_ids=user_ids,
            start=time_range,
            end=time_range,
            batch_size=1000  # Avoid memory issues
        )
        # Handle missing data gracefully
        clean_data = [
            d for d in raw_data 
            if d.is_valid() and d.engagement_score is not None
        ]
        if not clean_data:
            logger.warning(
                f"No valid engagement data for {len(user_ids)} users "
                f"in range {time_range}"
            )
            return None
        # Use standard library (not reimplementing median from scratch)
        scores = [d.engagement_score for d in clean_data]
        return {
            'median_engagement': median(scores),
            'total_users': len(clean_data),
            'data_quality': len(clean_data) / len(raw_data),
            'timestamp': datetime.utcnow().isoformat()
        }
    except DatabaseConnectionError as e:
        logger.error(f"Database connection failed: {e}")
        # Trigger alert, return cached data, or fail gracefully
        return get_cached_metrics()
    except Exception as e:
        # Never let unknown errors crash the service
        logger.exception("Unexpected error in engagement analysis")
        return None

Notice something? The interview problem tests whether you memorized heap operations. The real problem tests whether you can:

• Write maintainable, production-ready code
• Handle errors gracefully
• Think about performance at scale
• Use appropriate logging
• Make pragmatic decisions (use statistics.median, don’t reimplement it!)
• Consider operational concerns These are completely different skill sets.

The Alternative Universe: Practical Assessments

Some companies have had enough of the charade and are trying something radical: testing whether you can actually do the job. Here’s what a modern, practical coding assessment might look like:

"""
Assignment: Implement a rate-limiting middleware for our API
Context: We're seeing abuse on our public API. Implement a simple
rate limiter that:
- Limits requests to 100 per hour per API key
- Returns appropriate HTTP status codes
- Handles concurrent requests safely
- Includes basic tests
You have 2-3 hours. Use whatever resources you normally would—
Stack Overflow, documentation, AI tools, whatever. We want to see
how you actually work.
We'll review:
- Code quality and structure
- Error handling
- Testing approach
- How you use tools and documentation
- Your decision-making process
"""
from datetime import datetime, timedelta
from collections import defaultdict
from threading import Lock
from typing import Optional
import time
class RateLimiter:
    def __init__(self, max_requests: int = 100, window_seconds: int = 3600):
        self.max_requests = max_requests
        self.window_seconds = window_seconds
        self.requests = defaultdict(list)
        self.lock = Lock()
    def is_allowed(self, api_key: str) -> tuple[bool, Optional[int]]:
        """
        Check if request is allowed under rate limit.
        Returns: (is_allowed, seconds_until_reset)
        """
        with self.lock:
            now = time.time()
            cutoff = now - self.window_seconds
            # Clean old requests
            self.requests[api_key] = [
                req_time for req_time in self.requests[api_key]
                if req_time > cutoff
            ]
            current_count = len(self.requests[api_key])
            if current_count >= self.max_requests:
                oldest_request = min(self.requests[api_key])
                retry_after = int(oldest_request + self.window_seconds - now)
                return False, retry_after
            self.requests[api_key].append(now)
            return True, None
# Example Flask integration
from flask import Flask, request, jsonify
app = Flask(__name__)
rate_limiter = RateLimiter()
@app.before_request
def check_rate_limit():
    api_key = request.headers.get('X-API-Key')
    if not api_key:
        return jsonify({'error': 'Missing API key'}), 401
    allowed, retry_after = rate_limiter.is_allowed(api_key)
    if not allowed:
        return jsonify({
            'error': 'Rate limit exceeded',
            'retry_after': retry_after
        }), 429

This assessment tests real skills:

• Can you build something practical?
• Do you understand concurrency primitives?
• Can you integrate with existing frameworks?
• Do you write tests?
• How do you handle edge cases? But here’s the catch: it’s harder to standardize, harder to calibrate, and yes, easier to cheat on with AI. There’s no perfect solution here.

The Path Forward: What Actually Works

After ranting about everything that’s broken, I should probably offer some constructive ideas. Here’s my opinionated take on what a better interview process might look like:

Step 1: Phone Screen (30 minutes)

Skip the coding. Just talk. Can this person communicate? Do they understand basic technical concepts? Are they actually interested in the role? You’d be shocked how many candidates fail this basic filter.

Step 2: Practical Take-Home (3-4 hours max)

Give them a real problem from your domain. Not FizzBuzz, not implementing a binary tree, but something like:

• “Build a simple cache invalidation system”
• “Implement a data pipeline that handles out-of-order events”
• “Create a feature flag service with an API” Let them use whatever tools they want. The internet exists. AI exists. They’ll use these things on the job anyway.

Step 3: Code Review Discussion (60 minutes)

Here’s where you catch AI cheating: have them walk through their solution in detail. Ask them to:

• Explain design decisions
• Identify potential issues
• Propose improvements
• Discuss tradeoffs Someone who ChatGPT-ed the whole solution will crumble here. Someone who built it themselves will shine.

Step 4: System Design or Domain Deep-Dive (60 minutes)

Now you can do your traditional system design interview or go deep on domain knowledge relevant to the role. At this point, you’ve already validated they can code.

Step 5: Team Fit and Values (30-45 minutes)

Because being a brilliant asshole who can’t collaborate is worse than being mediocre but coachable.

The Reality Check

Look, I’m not naive enough to think Big Tech is going to read this article and revolutionize their hiring process. They won’t. The machine is too big, the inertia too strong, the risk of change too high. But you might be at a smaller company. You might be the one designing the interview process. You might have the freedom to try something different. And here’s what I want you to understand: the goal isn’t to eliminate false negatives. There’s no interview process that catches every great candidate. The goal is to find a process that:

1. Provides signal (actually predicts job performance)
2. Respects candidates’ time (they have jobs too)
3. Reflects your actual work (attracts the right people)
4. Scales reasonably (you can’t spend 20 hours per candidate)
5. Adapts to reality (AI is here, deal with it)

The Uncomfortable Questions You Should Be Asking

Before you run your next interview, ask yourself: Would I pass my own company’s interview process today? Be honest. If you’re like most senior engineers, the answer is probably “not without weeks of LeetCode grinding.” That should tell you something. What percentage of my day-to-day work resembles our interview questions? If it’s less than 20%, your interview is testing the wrong things. Have we measured if our interview process actually predicts job performance? Most companies haven’t. They just assume it does because everyone else does it. Are we accidentally filtering out great engineers who refuse to play the game? The engineer who’s phenomenal at building production systems but can’t be bothered to memorize tree traversal algorithms—are we okay with losing them?

My Controversial Take

Here’s something that’s going to upset people: I think the pendulum has swung too far in both directions. Big Tech’s LeetCode grind is disconnected from reality, but the alternative—completely unstructured “just build something cool” assessments—creates its own problems. They’re hard to calibrate, potentially discriminatory (favoring candidates with more free time), and yes, easier to game with AI. What we need is structured practicality:

• Standardized practical problems (not algorithms)
• Clear evaluation rubrics
• Reasonable time constraints
• Real tools and real frameworks
• AI-resistant validation through discussion

The Action Plan

If you’re in a position to influence your company’s interview process, here’s what you can do tomorrow:

For Big Tech Believers

# Add this to your loop
def post_interview_analysis():
    """Track and analyze your interview outcomes"""
    candidate_scores = get_interview_scores(timeframe='last_quarter')
    job_performance = get_performance_ratings(same_candidates)
    correlation = calculate_correlation(
        candidate_scores, 
        job_performance
    )
    if correlation < 0.3:  # Weak correlation
        print("Houston, we have a problem")
        print("Our interviews don't predict job performance")
        print("Time to rethink this")

Actually measure if your process works. Don’t just assume it does because Google does it.

For Practical Assessment Advocates

# Build guardrails
def validate_practical_assessment(submission, candidate):
    """Ensure submissions are actually theirs"""
    # Step 1: Initial submission
    initial_code = submission.code
    # Step 2: Live code review
    live_discussion_notes = conduct_code_review(candidate)
    # Step 3: Extension task
    extension_code = ask_to_extend_solution(
        candidate,
        original_code=initial_code,
        new_requirement="Add rate limiting per user"
    )
    # If they can't explain or extend their own code,
    # that's your signal
    consistency_score = analyze_consistency(
        initial_code,
        live_discussion_notes,
        extension_code
    )
    return consistency_score

Make it hard to cheat without making it a hostile environment.

For Everyone

Start small. Pick one interview in your loop and experiment with making it more practical. Measure the results. Iterate. You don’t have to revolutionize everything overnight.

The Takeaway

Your coding interview process is probably broken. Mine too. Everyone’s is, in different ways. The question isn’t whether it’s perfect—nothing is. The question is: are you making it better or just accepting the status quo because “that’s how it’s always been done”? In 2025, we have better tools, better understanding, and more options than ever before. We also have new challenges (hello, AI cheating). The companies that will win the talent war aren’t the ones with the hardest interviews—they’re the ones with the most effective interviews. There’s a difference. The algorithmic interview isn’t going away tomorrow. But it doesn’t have to be the only game in town. And for the love of all that is holy, if you’re going to keep asking LeetCode questions, at least acknowledge that it’s a game, set clear expectations, and provide resources for candidates to prepare. Because here’s the thing: transparency is respect. If you want candidates to jump through hoops, at least be honest about what hoops they need to jump through and why. The interview process is broken. We all know it. The question is: what are you going to do about it? Now if you’ll excuse me, I need to go practice implementing a red-black tree, you know, just in case I ever need to… oh wait, I never will. And neither will you. And that’s exactly the problem. What’s your take? Have you found an interview approach that actually works? Or are you still traumatized from that time someone asked you to implement a LRU cache in 20 minutes? Drop your war stories in the comments—I promise I won’t judge. Much.