Software engineering interviews are a distinct discipline. Candidates who succeed at Google, Amazon, Airbnb, and LinkedIn are not just good engineers — they are engineers who can think out loud under pressure, articulate complexity tradeoffs in real time, and connect technical decisions to business outcomes. The interview is a performance, and like any performance, it is improved by structured practice.
Software Interview uses AI-powered case practice to sharpen exactly these skills. Each session presents a realistic coding problem, system design scenario, or behavioral question calibrated to the evaluation dimensions of top engineering companies. The AI coach gives you specific feedback on your algorithm selection, complexity analysis, design tradeoffs, and leadership principles storytelling — not just whether your code compiles.
How it works
- Practice coding cases modeled on real interview questions from Google, Amazon, Airbnb, and LinkedIn
- Get AI-powered feedback on algorithm selection, time and space complexity, and code quality
- Build skills across data structures, system design, distributed systems, and behavioral frameworks
- Track your progress across 20+ software engineering competencies with adaptive difficulty
Why software engineering interviews need dedicated prep
Grinding LeetCode problems is not the same as interview preparation. Top engineering companies evaluate more than whether you can produce a correct solution — they assess how you approach ambiguity, communicate your reasoning, identify and articulate tradeoffs, and connect low-level technical decisions to system-wide implications. Candidates who practice on raw problem sets often freeze when interviewers ask follow-up questions about complexity, edge cases, or alternative approaches.
The AI coach pushes you on the dimensions that distinguish strong candidates: Can you identify the optimal data structure in the first 90 seconds? Can you explain why your O(n log k) heap solution outperforms the naive O(n log n) sort for top-K problems? Can you reason about cache hit rates and serialization overhead when diagnosing a latency regression? This level of fluency separates candidates who get offers from those who reach the final loop but stall on depth.
Built for aspiring software engineers
Software Interview is designed for engineers at all levels preparing for SWE roles at top technology companies — whether you are a new grad targeting Google and Amazon for your first industry role, a mid-level engineer preparing for the senior bar at Airbnb or LinkedIn, or an experienced engineer working toward staff and principal positions. Structured case practice builds interview fluency faster than self-study alone, and the AI feedback loop accelerates the skills that matter most for the roles you want.