SDE hiring follows a predictable pattern: resume review, recruiter screen, then technical rounds where your senior engineers spend an hour asking the same coding, system design, and debugging questions they asked last week. This guide explains how AI interviews handle that first technical screen, what they assess, and whether they fit your hiring pipeline.
Can AI Actually Interview SDEs?
Hiring managers question whether AI can evaluate the depth expected from software development engineers. That skepticism is reasonable. The SDE role demands strong coding ability, system design understanding, debugging skills, and clear technical communication.
AI interviews handle first-round SDE screens effectively. They present coding challenges that execute against test cases, probe understanding of system design concepts, and evaluate debugging methodology. The AI tracks how candidates reason through problems, not just whether they reach correct answers. For debugging tasks, it introduces issues and observes how systematically candidates isolate and resolve them.
Human evaluation still matters for culture fit, team dynamics, and final hiring decisions on senior roles. But the repetitive first technical screen works well as an AI-administered assessment.
Why Use AI Interviews for SDEs
SDE hiring has a recurring cost: your best engineers spend hours on screens instead of building systems. The skills you need to verify, coding proficiency, system design thinking, and debugging ability, can be tested without a human interviewer repeating the same process.
Code Execution with Test Cases
AI interviews run candidate code against actual test cases. You see whether their solutions work and handle edge cases, not just whether they explain approaches convincingly.
System Design Assessment
The AI presents architectural scenarios and evaluates reasoning. How would you design a rate limiter? What tradeoffs exist between different caching strategies? Candidates explain their thinking while the AI evaluates depth.
Debugging Under Observation
The AI introduces bugs and watches how candidates diagnose issues. Do they add logging strategically? Do they trace logic methodically? This reveals practical troubleshooting ability.
Engineering Time Recovery
Teams running many screens monthly lose substantial engineering capacity. AI interviews return that time while maintaining technical assessment quality.
See a Sample Engineering Interview Report
Review a real Engineering Interview conducted by Fabric.
How to Design an AI Interview for SDEs
An effective AI interview for SDEs combines coding challenges, system design questions, and debugging exercises. The balance depends on seniority level and your team's specific requirements.
Coding Challenges
Present algorithmic problems requiring candidates to write and execute code. Include data structure usage, optimization considerations, and edge case handling. The AI monitors solution structure and efficiency.
System Design Questions
For experienced candidates, include architecture scenarios. Ask how they would design specific systems and what tradeoffs they would consider. The candidate explains their reasoning while the AI evaluates depth.
Debugging Exercises
Provide code with intentional bugs. Observe how candidates diagnose and fix issues. Do they trace logic systematically or guess randomly? This reveals practical troubleshooting skills.
Technical Communication
Ask candidates to explain their code and decisions as they work. Strong SDEs articulate why they chose particular approaches, not just what they implemented.
Interview length typically ranges from 30-60 minutes. Afterwards, your team receives structured scores covering each assessed skill area.
AI Interviews for SDEs with Fabric
Most AI interview tools record video responses to static prompts. Fabric runs live coding interviews where candidates write and execute code against real test cases, simulating an actual technical screen.
Live Code Execution
Fabric executes code in 20+ languages with real runtime environments. Candidates write in a browser-based IDE, run solutions, and see results immediately. No simulated environments or syntax-only checks.
Adaptive Questioning
When candidates submit working code, the AI asks about time complexity, edge cases, or alternative approaches. When they struggle, it provides calibrated hints that reveal whether issues are syntactic or conceptual.
Structured Scorecards
After each interview, your team receives scores for code correctness, code quality, system thinking, and communication. Each score includes specific evidence from the interview.
Cheating Detection
Fabric monitors tab switches, paste behavior, typing patterns, and timing anomalies. Flagged interviews surface for human review with specific timestamps of concerning activity.
Get Started with AI Interviews for SDEs
Try a sample interview yourself or talk to our team about your hiring needs.