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>