Beyond the Resume: A Technical Founder's Hiring Framework

●TL;DR

85 years of personnel selection research shows: work samples (r = 0.54) predict performance 3x better than years of experience (r = 0.18). Structured interviews beat unstructured by 34%. The cost of a bad hire is 30% of first-year salary plus the damage they do. Stop reading resumes. Start testing actual work.

Part of the Engineering Leadership Guide ... from solo founder to CTO leading 50+ engineers.

●The Resume Illusion

Every technical founder's hiring process starts the same way: reviewing resumes. Years of experience. Company names. Technology lists. Maybe a cover letter nobody reads.

This process feels productive. It feels like filtering. It feels like diligence.

It's nearly useless.

Frank Schmidt and John Hunter's meta-analysis of personnel selection methods... aggregating 85 years of research across thousands of studies... established the predictive validity of common hiring practices. The results are uncomfortable for anyone who's ever read a resume:

Let that sink in. Years of experience... the primary filter on most job listings... has a validity coefficient of 0.18. A validity of 0.18 means the correlation between experience and job performance is barely above random chance.

A developer with ten years of experience might have ten years of progressive growth. Or they might have one year repeated ten times. The resume doesn't tell you which.

●The Predictive Validity Hierarchy

If you can only do one thing to improve your hiring, use work samples. If you can do two things, add structured interviews. Everything else is noise.

Work Samples: The Gold Standard

Work sample tests predict job performance better than any other method because they directly measure what you care about: can this person do the work?

The key is designing tasks that mirror actual responsibilities:

Bad Work Sample: "Reverse a linked list on a whiteboard."

This tests algorithm memorization under artificial pressure. Unless you're hiring for competitive programming, it predicts nothing about job performance.

Good Work Sample: "Here's a Next.js component with a bug. The loading state shows briefly, then disappears before data arrives. Find and fix the bug. You have 90 minutes and access to the internet."

This tests:

• React state management (actual skill needed)
• Debugging approach (how they diagnose problems)
• Resource usage (can they find relevant documentation)
• Time management (shipping something imperfect vs. perfecting nothing)

The work sample should be:

• Time-boxed (prevent perfectionism, respect candidate time)
• Representative of daily work
• Evaluated against a rubric, not "did it work"
• Focused on approach as much as outcome

Structured Interviews: Removing the Gut

Most interviews are unstructured conversations where the interviewer decides based on "fit" or "vibe." This is how bias enters. People hire people who remind them of themselves.

Structured interviews control for this by:

1. Consistent Questions: Every candidate at the same level answers the same questions.
2. Rubric Scoring: Answers are evaluated against predefined criteria, not compared to other candidates.
3. Behavioral Focus: "Tell me about a time when..." extracts evidence of past behavior, which predicts future behavior.

Google's internal research confirmed these findings. After analyzing thousands of interviews, their People Analytics team found that brainteasers had zero predictive validity. They abandoned them entirely.

Instead, they implemented structured behavioral interviews. Every interviewer asks from a question bank. Every answer is scored 1-4 on specific criteria. The hiring decision aggregates scores across interviewers.

General Mental Ability: The Controversial Predictor

GMA (essentially IQ) is a strong predictor of job performance, especially for complex roles like software engineering. But it's controversial because:

• Testing can introduce cultural bias
• Proxy measures are unreliable
• It feels discriminatory even when technically valid

Most organizations avoid explicit cognitive testing. Instead, work samples implicitly measure cognitive ability... solving novel problems requires it. A well-designed work sample captures GMA benefits without the baggage.

●Designing Effective Work Samples

The work sample is your highest-leverage hiring investment. Here's how to design one that actually predicts performance.

Mirror Real Work

The task should be something the candidate would actually do in the role. For a full-stack developer:

Production-Like Task:

You're building a user dashboard feature. The designer has provided a
Figma mockup (linked). The API endpoint /api/user/:id returns user data.

Build a React component that:
1. Fetches and displays user data
2. Shows appropriate loading and error states
3. Matches the design reasonably well

You have 3 hours. Use whatever libraries you'd use in a real project.

This tests:

• API integration (will they async/await correctly?)
• State management (loading, error, success states)
• CSS/styling ability
• Code organization
• Library choices

Time-Box Ruthlessly

Respect candidate time and prevent perfectionism. 2-4 hours is reasonable for a take-home. 45-90 minutes for live coding.

Shorter assessments favor experienced developers who work efficiently. Longer assessments favor candidates with more free time. Neither is inherently better... just be aware of the tradeoff.

Evaluate Process, Not Just Output

A rubric might look like:

The candidate who writes imperfect code but clearly explains their tradeoffs often outperforms the candidate with perfect code and no explanation.

Provide Context and Resources

Real work happens with context and resources. Candidates should have:

• Access to documentation (internet, official docs)
• The ability to ask clarifying questions
• Context about why the feature matters

Artificial constraints ("no internet") test memory, not engineering ability. You want engineers who can find answers, not engineers who memorized the API.

●The Pair Programming Variant

Live coding assessments have downsides... they create artificial pressure and favor candidates who perform well under observation. The pair programming variant addresses this.

Instead of watching someone code alone, work with them:

1. You provide the starter code
2. You explain the problem
3. They drive (write code)
4. You navigate (answer questions, provide hints)

This tests:

• Communication skills (can they explain their thinking?)
• Coachability (do they take hints gracefully?)
• Collaboration (would you want to work with them daily?)

The artificial pressure drops because you're helping, not judging. You learn how they think, not just whether they memorized the solution.

●Structured Interview Framework

For roles beyond junior, technical ability is necessary but not sufficient. You need to assess:

• System design thinking
• Communication skills
• Past behavior under pressure
• Culture fit (actual, not "would I have a beer with them")

System Design Questions

For mid-level and above, system design reveals architectural thinking:

Example: "Design a URL shortener. Walk me through the architecture."

Follow-ups probe depth:

• "How would you handle 1M requests/second?"
• "How do you prevent abuse?"
• "How do you expire old links?"
• "What would you do differently with 10x the engineering time?"

Grade against rubric:

• Did they ask clarifying questions? (Requirements gathering)
• Did they start with data modeling? (Fundamentals first)
• Did they consider edge cases? (Production thinking)
• Did they acknowledge tradeoffs? (Mature reasoning)

Behavioral Questions

Past behavior predicts future behavior. Behavioral questions extract evidence:

Strong Behavioral Questions:

• "Tell me about a time when you had to debug a production incident under pressure."
• "Describe a technical decision you made that you later regretted."
• "Tell me about a conflict with a colleague and how you resolved it."

Weak Behavioral Questions:

• "What would you do if..." (hypothetical, not behavioral)
• "What's your greatest weakness?" (rehearsed answers)
• "Where do you see yourself in 5 years?" (irrelevant to job performance)

Look for specificity. Vague answers ("I'm a great team player") score lower than specific examples ("When our deployment pipeline failed during a launch, I stayed late to help the DevOps engineer debug the issue, even though it wasn't my area").

The STAR Framework

Score behavioral answers using STAR:

• Situation: Did they set context?
• Task: What was their specific responsibility?
• Action: What did they personally do?
• Result: What was the outcome?

Candidates who use "we" throughout might be claiming team accomplishments. Candidates who focus only on results might be exaggerating their contribution. Complete STAR responses indicate genuine experience.

●Red Flags in Technical Hiring

These patterns predict poor performance:

Title Inflation

"Senior Developer with 3 years of experience" is a red flag, especially in offshore markets. The title doesn't match industry norms.

Probe with: "What responsibilities did that 'Senior' title include?" Look for architecture decisions, code review authority, mentoring juniors... not just writing code for longer.

Cannot Explain Own Code

In work sample review, ask: "Walk me through this function. Why did you choose this approach?"

If they struggle to explain their own work, they either didn't write it (plagiarism) or don't understand what they wrote (concerning either way).

No Questions About the Problem Domain

Candidates who dive into coding without asking clarifying questions are optimizing for speed over correctness. In real work, this creates features that miss requirements.

Strong candidates ask:

• "Who's the user for this feature?"
• "What happens if this fails?"
• "Are there performance requirements?"

Framework Over Fundamentals

"I've used React for 5 years" tells you nothing. Probe deeper:

"How does React's reconciliation algorithm decide when to re-render?"

If they can only describe using React but not how React works, they'll struggle when things break in unexpected ways.

No Production Stories

Ask: "Tell me about a time when something broke in production and you had to fix it."

Candidates with real production experience have war stories. The details might be vague (it was years ago) but the pattern... detect, diagnose, fix, post-mortem... should be familiar.

Candidates who've never handled production systems will either deflect or fabricate. Both are concerning.

●Green Flags

These patterns predict strong performance:

Admits Uncertainty

"I'm not sure, but I'd approach it by..."

This indicates:

• Self-awareness (knows their limits)
• Problem-solving orientation (focuses on finding answers)
• Honesty (doesn't bullshit)

Engineers who admit uncertainty learn faster than engineers who fake confidence.

Tradeoff Thinking

"The advantage of this approach is X, but the downside is Y."

Engineering is about tradeoffs. Candidates who present solutions as purely good haven't thought deeply about them.

Ask follow-ups: "What would make you choose the other approach?" This reveals whether they understand both sides or just memorized an answer.

Questions First

Candidates who ask clarifying questions before coding treat the interview like real work. They want to solve the right problem, not just any problem quickly.

In work samples, look for comments like: "I assumed X because... but I would clarify this in a real project."

System Awareness

Strong candidates understand how their code fits into the larger system:

• "This would need rate limiting at the API gateway level"
• "We'd want to log this for debugging"
• "This could be cached if it becomes a bottleneck"

This indicates production experience and architectural thinking beyond the immediate task.

Failure Reflection

Ask about past failures. Strong candidates:

• Own their mistakes ("I should have written tests first")
• Explain what they learned ("Now I always...")
• Don't blame circumstances or colleagues

Weak candidates deflect or never seem to have failed (suspicious).

●The Polyglot Strategy

Some organizations hire for fundamentals and train for stack. The logic: specific frameworks change; problem-solving ability persists.

When This Works

• Strong Onboarding: The organization can bring someone up to speed quickly
• Experienced Team: Seniors available to mentor and review
• Low-Risk Projects: Initial assignments allow for learning curve
• Cultural Match: The candidate has proven ability in something

Stripe and Uber have historically hired language-agnostic engineers for their engineering DNA and taught specific stacks during onboarding.

When This Fails

• Startups Without Seniors: Nobody to teach the new stack
• Immediate Delivery Pressure: No time for learning curve
• Niche Technologies: Some stacks (Kubernetes administration, ML ops) require specific experience

For most startups making their first hires, hiring for your specific stack reduces risk. You can't afford months of ramp-up when you have months of runway.

●The Cost of Getting It Wrong

The Society for Human Resource Management estimates the cost of a bad hire at 30% of first-year salary. For a $150,000 developer, that's $45,000 in direct costs.

But the hidden costs are larger:

Architectural Damage

A bad senior developer making architectural decisions can set patterns that burden the team for years. The monolith they chose when microservices made sense. The database schema that doesn't support multi-tenancy. The authentication system that needs to be replaced.

Fixing these decisions costs multiples of the original implementation.

Team Velocity Drain

Bad developers consume senior attention:

• Extended code reviews (finding and explaining issues)
• Pairing to fix bugs
• Cleaning up after them
• Documenting things they should have documented

If a senior spends 20% of their time supporting an underperformer, that's effectively a 20% salary overhead on the bad hire.

Morale and Attrition

Good developers don't want to work with bad ones. They:

• Get frustrated cleaning up messes
• Resent the apparent tolerance of underperformance
• Start looking for teams that maintain standards

The cost of attrition... recruiting, onboarding, lost productivity... dwarfs the salary of the developer who caused it.

●The Hiring Checklist

Before Opening the Role

• Is the work sample designed and tested?
• Is the interview rubric documented?
• Who will evaluate candidates? Are they calibrated on the rubric?
• What's the timeline? Don't let good candidates wait.

Resume Screen (Minimal Time)

• Do they have any relevant experience?
• Are there obvious mismatches (wrong timezone, wrong level)?
• Move quickly... this is low-value filtering

Work Sample (Highest Weight)

• Did they complete the task?
• How did they approach the problem?
• Can they explain their decisions?
• Would you want to maintain this code?

Structured Interview (High Weight)

• Behavioral questions with STAR framework
• System design (for mid-level and above)
• Culture and communication fit
• Questions they ask you (reveals research and curiosity)

Reference Checks (Low Weight but Required)

• Did they verify tenure and title?
• Can references speak to specific contributions?
• Any hesitation or qualified praise?

Decision

• Score candidates against rubric, not each other
• Debrief with all interviewers before deciding
• If uncertain, don't hire... a false positive is worse than a false negative

●Conclusion

The resume is not the candidate. Years of experience is not skill. A good interview is not a good hire.

The data is clear: work samples predict performance 3x better than experience. Structured interviews beat unstructured by 34%. The methods that feel rigorous... reading resumes, having coffee chats... are nearly useless.

For technical founders who can't afford bad hires, this matters. Every mis-hire burns runway, slows delivery, and risks team morale. Every good hire compounds... they ship features, mentor others, and attract more good engineers.

Invest in your hiring process. Design work samples that mirror real work. Structure your interviews with rubrics. Ignore the years on the resume and evaluate what people can actually do.

The cost of getting it right is a few hours of process design. The cost of getting it wrong is months of cleanup.

Building your engineering team? I help technical founders evaluate candidates, structure interviews, and make hiring decisions they won't regret.

• Technical Advisor for Startups ... Strategic hiring guidance
• Full-Stack Development for Startups ... Team structure and growth
• Fractional CTO for Startups ... High-stakes hiring

●Continue Reading

This post is part of the Engineering Leadership Guide ... covering hiring, team structure, technical debt, and the IC to executive transition.

More in This Series

• Fractional CTO vs Full-Time ... When to hire vs contract
• First Engineering Team Playbook ... Your first 3 hires
• The Senior Developer Paradox ... Why expensive talent saves money
• IC to Tech Lead ... Leadership without the title
• Technical Debt Strategy ... Managing debt as a business decision

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