LayersRank

HIRE BACKEND ENGINEERS

Find Backend Engineers Who Can Actually Build Scalable Systems

Evaluate system design thinking, API architecture decisions, and debugging approaches with structured assessments designed specifically for backend hiring.

The Hiring Challenge

Backend engineers are the foundation of your technical architecture. A great backend engineer builds systems that scale, perform, and remain maintainable. A poor one creates technical debt that compounds for years.

The problem: backend skills are hard to evaluate in traditional interviews. A candidate can discuss microservices fluently without ever having built one. They can whiteboard a system design without understanding the operational reality.

Common Hiring Mistakes

Testing syntax, not judgment

Fizzbuzz doesn't tell you if they can design an API.

Focusing on current stack

Good engineers learn technologies. Great engineers make good decisions regardless of stack.

Ignoring debugging ability

Anyone can build greenfield. The job is often debugging, optimizing, and maintaining.

Skipping behavioral evaluation

Backend engineers work with frontend teams, product managers, and ops. Collaboration matters.

Evaluation Framework

What LayersRank Evaluates

Technical Dimension

45%

System Design

  • Trade-off reasoning (consistency vs. availability, complexity vs. simplicity)
  • Scalability thinking (what breaks at 10x, 100x load?)
  • Data modeling decisions

API Architecture

  • RESTful design principles
  • Error handling philosophy
  • Versioning and backward compatibility

Debugging & Operations

  • Systematic troubleshooting approach
  • Production incident handling
  • Performance optimization thinking

Behavioral Dimension

35%

Technical Communication

  • Explaining complex systems to non-technical stakeholders
  • Documentation practices
  • Code review philosophy

Collaboration

  • Working with frontend teams on API contracts
  • Cross-functional coordination
  • Handling disagreements on technical approach

Ownership

  • Taking responsibility for system reliability
  • Proactive problem identification
  • On-call and incident response attitude

Contextual Dimension

20%

Growth Trajectory

  • Learning approach for new technologies
  • Career goals alignment
  • Interest in the problem domain

Sample Questions

Sample Assessment Questions

1
technical

Walk me through how you would design an API rate limiting system. What trade-offs would you consider?

What this reveals: System design thinking, awareness of distributed systems challenges, trade-off reasoning between precision and performance.

2
technical

You're debugging a production issue where API response times have suddenly increased by 10x. Walk me through your approach.

What this reveals: Systematic debugging methodology, familiarity with observability tools, ability to stay calm under pressure.

3
technical

Describe a technical decision you made that you later regretted. What would you do differently?

What this reveals: Self-awareness, learning orientation, intellectual honesty about mistakes.

4
behavioral

Tell me about a time you disagreed with a frontend engineer about API design. How did you handle it?

What this reveals: Collaboration style, ability to advocate for technical positions while respecting other perspectives.

5
behavioral

Describe a situation where you had to maintain a system you didn't build. What was challenging?

What this reveals: Attitude toward legacy code, documentation practices, empathy for future maintainers.

Evaluation Criteria

What separates strong candidates from weak ones across each competency.

System Design

Great: Considers multiple approaches, articulates trade-offs clearly, thinks about failure modes
Red flags: Jumps to solution without analysis, ignores scale, no awareness of operational concerns

Code Quality

Great: Discusses maintainability, testing strategy, documentation
Red flags: Only talks about making it work, dismissive of code review

Debugging

Great: Systematic approach, uses data, isolates variables
Red flags: Random trial-and-error, blames external factors

Collaboration

Great: Seeks to understand other perspectives, communicates clearly
Red flags: Dismissive of frontend concerns, poor documentation attitude

Ownership

Great: Takes responsibility for reliability, proactive about issues
Red flags: "Not my problem" attitude, avoids on-call

How It Works

1

Configure your backend assessment

Use our template or customize for your stack

2

Invite candidates

They complete the assessment async (35-45 min)

3

Review reports

See scores with confidence intervals across all dimensions

4

Make better decisions

Know exactly where to probe in final rounds

Time to first assessment: under 10 minutes

Pricing

PlanPer AssessmentBest For
Starter₹2,500Hiring 1-5 backend engineers
Growth₹1,800Hiring 5-20 backend engineers
EnterpriseCustomHiring 20+ backend engineers

Start Free Trial — 5 assessments included

Frequently Asked Questions

How long does the backend assessment take candidates?

35-45 minutes. Mix of video responses (system design, debugging scenarios) and written responses (technical explanations).

What if we use a specific tech stack?

The default assessment focuses on language-agnostic backend concepts. You can customize to include stack-specific questions.

How does this compare to HackerRank or Codility?

Those test coding speed. LayersRank evaluates engineering judgment. Different signals — many teams use both.

Can we see the questions before inviting candidates?

Yes. Full preview available after signup.

Ready to Hire Better?

5 assessments free. No credit card. See the difference structured evaluation makes.