How is proof of work different from traditional technical interviews?

Traditional interviews use generic problems like LeetCode or system design questions. Proof of work hiring uses real problems from your company's codebase, testing candidates against the actual challenges they will face on the job.

Does proof of work hiring reduce mis-hires?

Yes. By testing candidates on your actual environment, you see their real-world capabilities before hiring. This significantly reduces mis-hires because candidates demonstrate job-relevant skills rather than interview-specific skills.

How long does a proof of work assessment take?

Our three-layer assessment covers technical verification, ethics and safety culture fit, and live interview verification. Total time varies by role and is typically spread across multiple focused sessions. This is designed to respect candidate time while providing meaningful signal about their capabilities.

What is Proof of Work Hiring?

Q: What is proof of work hiring?

Proof of work hiring is a recruitment methodology where candidates are evaluated on real tasks from your actual codebase rather than generic coding puzzles or whiteboard exercises. This validates whether they can perform in your specific technical environment.

You hired a brilliant engineer. Great resume. Aced the LeetCode interview. Strong references.

Then they start. And within weeks, you realize they can solve abstract puzzles but struggle with real-world domain challenges.

Generic coding tests prove technical ability. They don't prove fit with real-world life sciences environments.

This is why we use proof of work hiring: we put candidates through pre-built standardized assessments with real-world, domain-specific problems and see if they can solve them.

The Problem with Generic Coding Tests

Most companies hire like this:

The Traditional Process:

Resume screening
Phone screen
LeetCode style coding challenge (invert a binary tree, solve the two sum problem)
System design interview
Hope they can work with your actual code

The result: You hire someone who can solve abstract puzzles but struggles with your legacy code, your tech stack, or your architecture.

Why Generic Tests Fail:

They test algorithm knowledge, not real world skills: Real-world life sciences codebases are not full of binary trees and graph traversals
They do not test for code quality: Can they write maintainable, production ready code?
They do not test architectural thinking: Can they navigate and extend an existing codebase?
They do not test debugging skills: Can they find and fix issues in unfamiliar code?
They are gameable: People practice LeetCode for months. That does not mean they can build products.

Our Approach: Real Code, Real Problems

We do not give candidates toy problems. We use pre-built standardized assessments with real-world, domain-specific challenges.

Here is How It Works:

Step 1: Candidates complete our pre-built standardized assessments designed for their specific domain (drug discovery, clinical AI, medical imaging, etc.)
Step 2: Assessments test real-world skills: molecular data pipelines, GxP compliance, regulatory documentation, clinical NLP — not abstract puzzles
Step 3: Candidates demonstrate they can solve the types of problems they will face on the job (2 to 4 hours)
Step 4: They submit their solution with documentation
Step 5: We review: Do they have the domain knowledge? Can they apply it? Is their solution production ready?

Key Difference:

Traditional tests ask: Can you solve abstract problems? We ask: Can you solve real problems in your specific domain?

Important: This Is Not a Basic Coding Test

Every candidate in our pool has already passed basic coding assessments.

What We Have Already Verified:

They can code: Passed algorithm and data structure tests
They understand ML and AI fundamentals: Completed framework specific challenges
They can ship: Demonstrated ability to build and deploy models
They communicate well: Clear documentation and explanation skills

Pre-built standardized assessments answer a different question:

This engineer is technically capable. But can they thrive in real-world life sciences environments, solving domain-specific problems under regulatory constraints?

Real Examples of Proof of Work Tests

Example 1: ML Model Optimization

Company: E commerce startup with recommendation engine

Problem: Our recommendation model is slow. Here is the code. Can you identify bottlenecks and suggest optimizations?

What it tests: Can they read ML code, profile performance, and make practical trade offs?

Time: 3 hours

Example 2: RAG System Debugging

Company: B2B SaaS with AI chatbot

Problem: Our RAG system is returning irrelevant results. Here is the retrieval code. Debug and fix it.

What it tests: Can they debug LLM applications, understand vector databases, and improve retrieval quality?

Time: 4 hours

Example 3: Feature Implementation

Company: Computer vision platform

Problem: Add batch processing support to our image classifier. Here is the existing single image code.

What it tests: Can they extend existing code, maintain architectural consistency, and write production ready features?

Time: 3 hours

The Bottom Line

Generic coding tests prove candidates can code. Pre-built standardized assessments with domain-specific problems prove they can succeed in life sciences environments.

Key takeaways:

All our candidates already passed basic coding tests. This is about fit with YOUR specific codebase
Real code reveals real ability better than any algorithm puzzle
You see exactly how they work before making a hiring decision
Candidates appreciate real problems over abstract brain teasers

Stop hiring based on LeetCode performance. Hire based on who can actually work in YOUR environment.

We Test Every Candidate on Real Code

That is why we only present candidates who have already proven they can work in your specific environment, not just pass generic tests.

See How We Vet Talent →