What Are Better Alternatives to LeetCode for Hiring Engineers?

Mar 15, 2026

The Problem

I’ve been on both sides of the hiring table, and I think most of us would agree that LeetCode questions aren’t great for evaluating candidates anymore. Last year, I watched a brilliant senior engineer with 15 years of experience bomb a coding interview because he hadn’t practiced inverting binary trees since college. We hired someone else who had memorized the solutions but struggled with actual system design.

The problem isn’t just that LeetCode is outdated—it’s that it actively filters out the wrong people. I’ve seen candidates spend weeks grinding algorithmic puzzles while neglecting the skills that actually matter on the job: debugging, collaboration, and working with AI tools.

A recent discussion I followed summed it up perfectly:

“Leetcode is dead for senior roles imo. System design + agent orchestration skills is where hiring should focus.”

And another comment that struck me:

“The Leetcode stuff never mattered for product work anyway, but now it really doesn’t.”

Why LeetCode Fails in 2026

I identified four critical flaws that make LeetCode-style interviews problematic:

Problem 1: Outdated Assessment Model
-------------------------------------
Algorithmic puzzles rarely reflect day-to-day engineering work.
Candidates spend weeks memorizing solutions rather than
demonstrating relevant skills.

Problem 2: AI Disruption
------------------------
With AI coding assistants, the ability to solve algorithmic
puzzles from memory has become less relevant. The focus should
shift to how candidates leverage and validate AI-generated code.

Problem 3: Senior Role Mismatch
-------------------------------
For experienced engineers, algorithmic challenges fail to assess
architectural thinking, system design capabilities, and leadership.

Problem 4: Product Development Disconnect
-----------------------------------------
Real product work involves debugging, code review, system
integration, and collaboration - not solving isolated problems.

My team experimented with alternatives last year. Here’s what we found.

Alternative 1: System Design Interviews

For senior roles especially, system design interviews correlate much better with actual job performance. I’ve seen candidates who struggled with LeetCode thrive when asked to architect a real system.

LeetCode Approach          |  System Design Approach
---------------------------|----------------------------
Isolated algorithm puzzle  |  Real-world architecture problem
Single correct answer      |  Multiple valid approaches
No business context        |  Requires domain thinking
Tests memorization         |  Tests decision-making
15-30 minutes              |  45-60 minutes

What System Design Reveals:
- Architectural thinking and scalability knowledge
- Trade-off analysis and decision-making
- Communication and collaboration skills
- Real-world experience patterns

When I conduct system design interviews, I look for:

Problem decomposition: Can they break down complex requirements?
Trade-off articulation: Do they explain WHY they chose one approach over another?
Scale awareness: Do they think about bottlenecks and edge cases?
Communication clarity: Can non-technical stakeholders understand their approach?

Alternative 2: Take-Home Assignments with AI Allowed

This one surprised me. My team has been experimenting with take-home challenges where we explicitly allow the use of LLMs. The results have been eye-opening.

Traditional Take-Home        |  AI-Allowed Take-Home
-----------------------------|----------------------------
Tests memorization           |  Tests AI collaboration
Time-consuming (4-8 hours)   |  Respectful (2-4 hours)
Artificial constraints       |  Realistic conditions
Penalizes AI users           |  Values validation skills

What We Actually Evaluate:
1. Code quality and organization
2. Understanding of AI-generated code
3. Ability to validate and fix AI output
4. Decision-making about when to use AI

A hiring manager I work with told me:

“We check more if a person can understand the code LLM generated and how they fix it if needed.”

This is the real skill now. I don’t need engineers who can write bubble sort from memory—I need engineers who can evaluate whether an AI-generated solution is correct, secure, and maintainable.

My recommended take-home structure:

Duration: 2-4 hours maximum
Scope: Real-world problem from your actual codebase
Tools: Any allowed (AI, Stack Overflow, documentation)
Evaluation Focus:
  - Solution correctness
  - Code readability
  - Decision documentation
  - AI usage explanation (what they accepted, rejected, modified)
Follow-up: 30-minute discussion about their approach

Alternative 3: Code Review Exercises

I started using code review exercises after realizing that reviewing code is a huge part of what engineers actually do. Why not test that directly?

How it works:

Present candidates with existing code (with intentional issues)
Ask them to review it as they would in a normal PR
Observe what they catch and how they communicate feedback

Review Request: User Authentication Module

Candidate sees code with:
- Potential SQL injection vulnerability
- Missing error handling
- Unclear variable names
- No input validation
- Missing tests

What I Look For:
- Security awareness (do they spot the injection risk?)
- Communication style (constructive or harsh?)
- Prioritization (what do they flag as critical vs nice-to-fix?)
- Domain knowledge (do they suggest appropriate fixes?)

This approach reveals collaboration skills that LeetCode cannot measure. I’ve seen candidates who aced algorithmic puzzles leave destructive, unhelpful code review comments—that’s a red flag for team fit.

Alternative 4: Agent Orchestration Assessments

This is the newest addition to my interview toolkit, and I think it’s the most forward-looking. As AI becomes central to development, the ability to direct and work with AI agents is becoming a critical skill.

Skill Being Tested          |  How to Assess
----------------------------|----------------------------------
Prompt Engineering          |  Give a vague task, observe prompt refinement
Output Validation           |  Provide AI output with subtle bugs
Tool Selection              |  Ask them to choose appropriate AI tools
Iterative Improvement       |  Show failing AI output, ask for fixes
Error Recovery              |  Simulate AI tool failure scenarios

Sample Exercise:
"Here's a PR that was mostly generated by Claude. Review it,
identify any issues, and explain what prompts you would use
to fix them."

I’ve found that engineers who excel at agent orchestration tend to be:

Better at breaking down complex problems
More thoughtful about requirements
More effective communicators
Faster at iteration and debugging

Alternative 5: Collaborative Debugging Sessions

This is my favorite for evaluating real-world problem-solving. I set up a live debugging session where we work through an actual bug together.

Setup:
- Real bug from your codebase (sanitized if needed)
- 30-45 minute paired debugging session
- Full access to logs, tools, documentation

What I Observe:
- How they approach unfamiliar code
- What questions they ask
- How they use debugging tools
- Whether they test their hypotheses
- Communication throughout the process

Red Flags:
- Jumping to conclusions without investigation
- Not asking clarifying questions
- Ignoring available tools and logs
- Working in isolation (not collaborating)

This approach is remarkably predictive of on-the-job performance. I’ve had candidates tell me afterward that it felt like a real work session—which is exactly the point.

Comparison: Which Method Works Best

Method                | Best For           | Time      | Predictive Value
----------------------|--------------------|-----------|------------------
LeetCode              | Junior screening   | 30-45 min | Low
System Design         | Senior roles       | 45-60 min | High
Take-Home (AI OK)     | All levels         | 2-4 hrs   | Very High
Code Review           | Mid-Senior roles   | 30-45 min | High
Agent Orchestration   | AI-forward teams   | 30-45 min | High (emerging)
Collaborative Debug   | All levels         | 30-45 min | Very High

Common Mistakes When Implementing Alternatives

I made these mistakes myself, so learn from my failures:

Mistake 1: No Standardization Without rubrics, alternative assessments become subjective. I created detailed scoring guides for each method.

Mistake 2: Too Much Time Investment Take-homes should respect candidate time. I capped ours at 4 hours maximum. Anything longer is disrespectful.

Mistake 3: Ignoring AI Reality Banning AI tools in assessments doesn’t reflect real work. My take-homes explicitly allow AI usage—I want to see how candidates use it.

Mistake 4: One-Size-Fits-All Different roles need different approaches. I use system design for seniors, debugging sessions for juniors, and code review for mid-level.

Mistake 5: Lack of Follow-up Always discuss the assessment in subsequent interviews. I ask candidates to walk me through their thinking—that reveals more than the solution itself.

My Current Interview Process

Screening (30 min):
- Resume review and initial conversation
- High-level technical discussion
- No LeetCode-style puzzles

Round 1 (45 min):
- Code review exercise OR collaborative debugging
- Focus on communication and problem-solving
- Real code, real scenarios

Round 2 (60 min):
- System design (for mid/senior) OR
- Take-home discussion (for all levels)
- Deep dive into decision-making

Round 3 (30 min):
- Agent orchestration exercise
- How they work with AI tools
- Validation and iteration skills

Final Round (60 min):
- Team fit and collaboration
- Career goals alignment
- Offer discussion

Why This Matters

Better hiring outcomes start with better assessment methods. I’ve seen teams dramatically improve their hiring success rate by moving away from LeetCode:

Better predictive validity: Alternative assessments correlate more strongly with on-the-job performance
Fairer process: Reduces advantage for candidates with time to grind LeetCode
Future-ready: Prepares teams for an AI-augmented development workflow
Higher retention: Candidates hired through relevant assessments tend to stay longer

The shift away from LeetCode isn’t just about fairness—it’s about finding engineers who can do the job in 2026, not 2016.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!