System Design Judge (SDOJ): Gamifying Interview Preparation

InterviewReady

timeline

July 2023 - Present

tools

  • Figma

  • Whimsical

  • Miro

  • Google Forms

  • Microsoft Clarity

  • Google Analytics

MY ROLE

Lead Product Designer responsible for end-to-end design process, from initial research to final implementation and iteration

results

  • 72,000+ practice sessions completed

  • 80% increase in user confidence

  • 4.7/5 user satisfaction rating

  • 30% higher retention vs traditional formats

skills & Methods

  • User Research & Surveys

  • Competitive Analysis

  • Iterative Design

  • User Journey Mapping

  • Gamification Design

  • Mobile-First Design

  • User Testing

  • Data Analysis

Overview

System Design Judge (SDOJ) revolutionizes how developers prepare for system design interviews by providing a gamified, realistic simulation platform. The project arose from a clear gap in the market: existing tools weren't adequately preparing candidates for the complexity and pressure of real interviews.

Problem

Traditional system design interview preparation tools offer static, oversimplified challenges that fail to replicate the dynamic, pressure-filled environment of actual interviews. This leaves candidates underprepared and lacking confidence.

Solution

SDOJ creates an immersive, game-like environment that simulates real system design interviews through adaptive challenges, time pressure, and decision-based progression, helping developers build both skills and confidence.

Defining the Problem

Developers need a more engaging and realistic way to practice system design interviews that builds both technical competence and interview confidence.

Opportunity

Create a platform that bridges the gap between theoretical knowledge and practical application through gamification and realistic simulation.

Hypothesis

By introducing game-like elements and realistic pressure scenarios, we can create a more engaging and effective preparation experience that better mirrors real interviews.

Research

  • Online survey with 164 developers

  • Competitive analysis of existing tools

  • User interviews with both junior and senior developers

  • Analysis of real interview feedback patterns

Persona

We identified two key user personas:

Iterations & Adjustments

The team explored various approaches to gamifying the interview experience, focusing on:

  • Time-based challenge structures

  • Decision tree navigation

  • Visual feedback systems

  • Progress tracking mechanics

First Proof of Concept

Our initial proof of concept focused on validating core mechanics and user engagement:

The Final Solution

Component Puzzle Mode

This mode challenges users to engage in multi-phase system design scenarios, where each decision impacts the overall system architecture. Users receive real-time feedback on how their choices affect the design, fostering iterative learning and decision-making skills. With progressive difficulty scaling, the challenges become increasingly complex, simulating real-world problem-solving environments. A time-based scoring system keeps users motivated and rewards efficient, effective solutions.

Capacity Estimation Mode

This mode immerses users in real-world estimation scenarios, where they perform time-pressured calculations based on industry-relevant problems. The tasks use industry-standard metrics to ensure practical relevance, and users can compare their results through comparative benchmarking, offering a clear perspective on performance relative to peers or standards.

Mobile-Optimized Experience

Designed for modern, busy users, this mode features a responsive design that adapts seamlessly to any mobile device, enabling on-the-go practice. Touch-optimized interfaces ensure smooth interaction, while simplified navigation makes it easy to focus on learning. With cross-device progress sync, users can pick up where they left off, no matter what device they’re using.

Retrospective Analysis

After completing challenges, users can delve into a detailed breakdown of their decisions, understanding the rationale and impact behind each one. Performance metrics highlight strengths and areas for improvement, while personalized improvement suggestions guide users toward better outcomes. To support continuous learning, tailored resources are provided for further exploration and skill enhancement.

Success Metrics

Aftermath & Retrospective

Learnings

  • Gamification elements significantly impact user engagement

  • Balance between challenge and guidance is crucial

  • Mobile accessibility drives consistent usage

  • Immediate feedback loops accelerate learning

  • User skill level diversity requires flexible difficulty scaling

Next Steps

  • Industry-specific challenge tracks

  • Peer review system implementation

  • Live mock interview feature

  • AI-powered feedback enhancement

  • Collaborative problem-solving modes

  • Advanced analytics for learning patterns

  • Custom challenge creation tools </antArtifact>

Copyright @2024

Made with

&

with ❤️ & ☕

Rishabh Singh

Copyright @2024

Made with

&

with ❤️ & ☕

Rishabh Singh

Copyright @2024

Made with

&

with ❤️ & ☕

Rishabh Singh