Why Hiring Senior Engineers Is Broken

hiring-senior-engineers The senior engineering interview process tests preparation, not ability. 63% of seniors get downleveled. Here's what actually predicts success and how to hire differently. hiring engineers, senior engineers, technical interviews, LeetCode, engineering hiring, interview process, software hiring

I've been on both sides of the interview table for thirty years. The process for hiring senior engineers has gotten worse, not better. We're testing the wrong things, taking too long, and losing the best candidates while debating their LeetCode performance.

The logic is sound on paper.

The numbers confirm what anyone who's recently job searched already knows: hiring is harder than ever for everyone involved. Candidates average 32 applications before getting hired, with many needing 100-200+ applications for a single offer. 63% of senior candidates receive downleveled offers. The median time-to-hire has stretched to months, not weeks.

Meanwhile, hiring managers complain they can't find qualified candidates. Both sides are frustrated. Something is fundamentally broken.

What Senior Engineering Interviews Test

The standard senior engineering interview loop at major companies includes:

• Algorithmic coding rounds. LeetCode-style problems under time pressure. Invert a binary tree. Implement an LRU cache. Find the shortest path.
• System design. "Design Twitter" or "Design a URL shortener" at a whiteboard with 45 minutes to fill.
• Behavioral interviews. "Tell me about a time you disagreed with your manager." STAR format answers expected.
• Culture fit. Often the most subjective round, testing whether the interviewer wants to work with you.

This process evolved at large tech companies and got cargo-culted across the industry. Startups with 20 employees run six-round interview processes designed for Google's hiring scale.

The problem is that this process tests the wrong things. Research from NC State found that whiteboard-style interviews measure performance anxiety more than coding ability. I've been hiring engineers since the 1990s, and as I've written before, algorithmic interviews measure interview preparation, not engineering ability. The best engineer I ever hired would have failed most FAANG interview loops.

The LeetCode Arms Race

According to The Pragmatic Engineer's 2025 analysis, engineers now face noticeably harder problems at every stage. One senior engineer who interviewed at Google in 2021 and again in 2024 reported that LeetCode "hard" problems, previously uncommon at Google, "seem to have become the norm."

Where companies once accepted "good enough" solutions, 82% now require flawless implementations with error handling under identical time limits.

This creates a preparation arms race. Candidates grind 200+ hours on LeetCode to prepare for interviews. That investment correlates with wanting the job. It doesn't correlate with job performance.

The candidate who spent six months grinding algorithms might be worse at production engineering than the candidate who spent those six months building systems. But the grinder passes the interview and the builder doesn't.

What Senior Engineers Actually Do

The job of a senior engineer bears little resemblance to interview performance:

Navigate ambiguity. Real requirements are messy. Senior engineers figure out what to build when nobody can clearly articulate what's needed. No interview problem has this ambiguity.

Make judgment calls about tradeoffs. Build or buy? Monolith or microservices? Perfect or shipped? These decisions shape outcomes more than algorithmic cleverness. But interviews reward algorithmic solutions to defined problems.

Unblock others. Senior engineers make teams more productive. They review code, mentor juniors, write documentation, establish patterns. This multiplier effect is invisible in individual performance assessments.

Deal with legacy systems. Most engineering work happens in existing codebases, not greenfield projects. Understanding unfamiliar code, safely making changes, and working within constraints - these are daily skills never tested in interviews.

Communicate across functions. Explaining technical concepts to non-technical stakeholders. Translating business requirements into technical plans. Disagreeing constructively. Writing proposals that get approved.

None of this appears in LeetCode rounds. System design comes closer but typically focuses on technical architecture rather than the judgment and communication that distinguish senior engineers.

The Downleveling Problem

According to Levels.fyi 2025 data, 63% of senior candidates receive downleveled offers. Meta's policy now requires 6+ years of experience for Senior SWE titles.

The dynamic is predictable: companies raise the bar to justify fewer hires. Candidates who would have been senior three years ago are now offered mid-level roles at senior-level expectations. Title compression masks what's actually happening - fewer jobs, more competition, lower offers.

For experienced engineers, downleveling is particularly demoralizing. You've led teams, shipped products, solved hard problems - and the interview process treats you like someone who needs to prove basic competence. The signal sent is clear: your experience doesn't matter; only your interview performance counts.

The Time Problem

Senior engineering interview processes at major companies routinely take 2-3 months from first contact to offer. That timeline works against everyone:

Candidates drop out. The best candidates have options. While you're scheduling round four, they've accepted an offer elsewhere. The process selects for candidates with fewer alternatives - precisely backwards.

Context decay. By the time a decision is made, interviewers struggle to remember specifics. The feedback loop is too long to be useful.

Business needs shift. The role you were hiring for in January might not exist in March. Teams reorganize. Budgets change. The hire that seemed urgent becomes frozen.

Meta's hiring process has become particularly problematic. One staff engineer who passed all technical rounds with strong positive feedback waited four months in team match limbo. By the time the match completed, all competing offers had expired.

The Knowledge Half-Life

Here's the temporal reality that makes keyword-based hiring backwards:

Junior engineers know things that expire in 18 months: React 19 patterns, Next.js 14 conventions, AWS Lambda specifics, the current hot framework.

Senior engineers know things that last 20 years: SQL fundamentals, TCP/IP networking, Linux internals, distributed consensus algorithms, debugging methodology, system design principles.

The Rule: Hire for Lindy Knowledge. If their expertise is entirely wrapped up in a framework released 2 years ago, they're not senior—they're just "current." The knowledge that matters compounds. The knowledge that impresses on resumes evaporates.

When you filter for "5+ years React experience," you're filtering for people who've spent half a decade on knowledge with an 18-month half-life. When you filter for "understands distributed systems," you're filtering for knowledge that will still be relevant in 2040.

What Actually Predicts Success

After decades of hiring, here's what I've found actually predicts engineering success:

Track record. What have they actually built? Did it work? Did it scale? Did it ship? Past performance predicts future performance better than any artificial test.

Communication clarity. Can they explain complex topics simply? Do they listen? Do they ask good questions? Senior engineering is increasingly about coordination, not just coding.

Learning velocity. How quickly do they get productive in unfamiliar territory? The specific technologies you use today will change. The ability to learn won't.

Judgment under uncertainty. When the answer isn't clear, how do they decide? Do they recognize when they don't know something? Can they proceed anyway?

Collaboration signals. How do they respond to disagreement? Do they build on others' ideas? Can they receive feedback without defensiveness? This is what I mean when I talk about what makes engineers actually effective beyond raw output.

These are harder to assess than LeetCode performance. They require talking to references, evaluating actual work, and having genuine conversations. But they're what actually matters.

What Better Looks Like

Better senior engineering hiring processes exist. They're harder to run, which is why they're rare:

Work sample tests. Give candidates a realistic task: review this PR, debug this failing test, add a feature to this small codebase. Evaluate the output, not performance under surveillance.

Here's an actual work sample I've used. Give this to a candidate and ask them to find the bug and explain why it would fail in a distributed system:

# Task: Find the concurrency bug and explain the production risk
import threading

class RateLimiter:
    def __init__(self, max_requests, window_seconds):
        self.max_requests = max_requests
        self.window = window_seconds
        self.requests = []
        self.lock = threading.Lock()

    def allow_request(self):
        now = time.time()
        # Clean old requests
        self.requests = [r for r in self.requests if r > now - self.window]

        if len(self.requests) < self.max_requests:
            self.requests.append(now)
            return True
        return False

The bug: self.requests is read and written outside the lock. Under concurrent load, you get race conditions—two threads can both read the count as "under limit," both append, and exceed the rate limit. Senior engineers spot this in minutes. They'll also note it fails completely across distributed instances without shared state. LeetCode grinders might not see it at all.

Paid project work. For senior roles, a paid day or week working on an actual problem. The candidate gets meaningful compensation. The company gets genuine signal about how they work. Both sides make informed decisions.

Deep reference checks. Not "did they work there?" but "what did they actually build and how did it perform?" Talk to peers, not just managers. Learn about collaboration style and technical judgment.

Here's the protocol I use. These questions bypass the "non-disparagement" boilerplate that makes most reference calls useless:

• "What's something they taught you?" If they can't name anything, that's signal about mentorship and knowledge sharing.
• "When did you disagree with them, and how did it resolve?" Reveals conflict style and willingness to change position.
• "If you were starting a company tomorrow and could hire three engineers, would they be one of them?" Forces a gut-level assessment that "would you work with them again" doesn't.
• "What kind of project would you NOT put them on?" Surfaces weaknesses without asking for negatives directly.
• "How did they handle the worst production incident you saw together?" Crisis behavior reveals character.

Portfolio review. For candidates with public work - open source contributions, blog posts, talks - discuss that work in depth. It's more representative than artificial exercises.

Realistic system design. Not "design Twitter" but "here's a specific problem we have, here are constraints, walk me through how you'd approach it." Look for judgment and communication, not memorized architectures.

When Standard Interviews Work

I'm not saying algorithmic interviews are always wrong. They make sense when:

• The role genuinely requires algorithmic thinking. Infrastructure at scale, search systems, compilers. If the job is optimizing data structures, test for it.
• You're hiring at massive scale. Google interviews 100,000+ candidates yearly. Standardized processes become necessary at that volume, even if imperfect.
• You have data showing correlation. If your organization has tracked interview performance against job performance and found signal, use what works for you.

But for most companies hiring a few senior engineers per year, the overhead of FAANG-style processes exceeds the benefit. The signal-to-noise ratio doesn't justify the cost.

The Middle Ground for Scale: If you're hiring 50+ engineers and can't do paid trials for everyone, use a tiered approach: (1) Async work sample as a filter—the rate limiter task above takes candidates 30 minutes and screens out 60% without any interviewer time. (2) Reserve paid trials or project days for final-round candidates only. (3) Use LeetCode sparingly—one algorithmic round, not four—and weight it at 20% of the decision, not 80%. This gives you standardization at scale without losing signal on judgment.

Audit your current hiring process against evidence-based practices:

The Market Paradox

The current market creates a paradox: companies claim they can't find qualified senior engineers while qualified senior engineers struggle to get hired.

The explanation is simple: the filtering process is broken. Companies reject candidates who would perform well because interview performance diverges from job performance. They hire candidates who interview well but underperform.

Then they conclude the market is bad rather than examining their process.

If you're hiring senior engineers and not finding qualified candidates, the problem might not be the candidate pool. It might be that your process filters out the people you want.

Hiring Process Quality Scorecard

"If you're hiring senior engineers and not finding qualified candidates, the problem might not be the candidate pool. It might be that your process filters out the people you want."