Coram AI Interview Questions (May 2026)
1 questions · 1p3a (1)
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Coram AI Full Stack and System Design Interview Experience
Question Details
Part 1: Coding Challenge - Wall Validation
Problem Statement Develop a function to validate a "wall" structure composed of 2D video tiles. The function receives a list of integers representing the (x, y) top-left coordinates of fixed-size 1x1 tiles.
Validation Criteria The function must return true if all the following conditions are met, and false otherwise: 1.
Non-Empty: The wall contains at least one tile. 2.
Origin: The shape starts at coordinate [0,0]. 3.
Shape: The tiles form a solid square or rectangle with no holes. 4.
Uniqueness: No tiles overlap (coordinates must be unique).
Input/Output Examples *
Valid: [[0,0], [0,1], [1,0], [1,1]] (2x2 Square) *
Valid: [[1,1], [0,1], [0,0], [1,0]] (Unsorted input is acceptable) *
Valid: [[0,0], [0,1]] (1x2 Rectangle) *
Invalid: [[0,0], [1,1]] (Contains holes) *
Invalid: [[0,0], [0,1], [1,0]] (Not a perfect rectangle) *
Invalid: [[0,0], [0,1], [0,1], [1,1]] (Contains overlaps) *** # Part 2:
System Design Camera Detection Events
Role: Full Stack Engineer
Data Context The system processes batched detection events from cameras running AI algorithms. *
Event Data: Camera hash (string), Detection type (Person, car, bicycle), Bounding Box (startX, startY, endX, endY), Timestamp. *
Existing Camera Metadata: Camera hash, Camera name, Last seen time.
Backend Requirements Design an architecture to ingest and process detection data with the following considerations: *
Ingestion: Handle batched events sent from cameras (e.g., every second). *
Storage: Persist detection events. *
Architecture: Define communication protocols, redundancy strategies, and scaling capabilities.
Frontend Requirements Design the following user interfaces: 1.
Landing Page (Camera List) * List all cameras in the system. * Display "Online" status based on last_seen_time (Online if seen < 30 seconds ago). * Handle dynamic updates (cameras added/removed). * Link to the Daily Activity page. 2.
Camera Daily Activity Page * Display detection analytics for a specific camera on a specific day. *
Required Metrics/Visualizations: * Timestamps for the first and last detection of each type (person/car/bike). * Distribution of detections per type throughout the day. * Concurrency analysis (e.g., identifying times when at least 4 people were detected simultaneously).
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Coram AI Interview Process Overview
The Coram AI interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Coram AI runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Coram AI coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
How To Use Coram AI Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Coram AI updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Coram AI reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Coram AI's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Coram AI Interview Mistakes
Reports tagged "no hire" at Coram AI consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.