What does Applied Intuition ask in software engineer interviews?

Applied Intuition SWE interviews typically cover data structures and algorithms, system design, and behavioral questions. Candidates report coding rounds using LeetCode-style problems, plus architecture design and leadership questions for senior roles. LeakCode aggregates verified reports from 63+ candidates.

How many rounds does Applied Intuition have in its interview process?

The Applied Intuition interview process typically includes a recruiter screen, a phone/online assessment round, and 4-5 onsite or virtual interviews covering coding, system design, and behavioral competencies. The exact number of rounds varies by role and team.

How hard is it to get a job at Applied Intuition?

Applied Intuition is highly selective. Candidates report pass rates in the single digits for competitive engineering roles. Strong data structures fundamentals, system design ability, and clear communication are required to advance past initial screens.

What topics should I study for a Applied Intuition interview?

Based on aggregated Applied Intuition interview reports: arrays and hash maps, trees and graphs, dynamic programming, system design (distributed systems, databases, caching), and behavioral questions using the STAR method. LeakCode shows exactly which topics appear most often.

Does Applied Intuition ask LeetCode hard questions?

Interview difficulty at Applied Intuition varies. Coding rounds typically include medium to hard difficulty algorithmic problems. System design questions are open-ended and evaluated on depth of reasoning. Browse real Applied Intuition interview questions on LeakCode to see the actual difficulty distribution.

Applied Intuition Interview Questions (2026-2027)

17 questions · 8 experiences · InterviewDB (17) · 1p3a (6) · LeetCode (2)

By year: 2025

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Software Engineer 24

Big Data Design: Architect a Scalable Pipeline for Petabyte-Scale Log Processing

Onsite

Question Details

Problem

You are asked to design a system that ingests 10 TB of application logs per day, processes them for anomaly detection and aggregation, and serves dashboards with sub-second query latency. Walk through the architecture.

Requirements:
  - Ingest: 10 TB/day, 100k events/sec peak
  - Processing: real-time aggregation + batch anomaly detection
  - Query: dashboard queries over last 7 days, p99 < 500ms
  - Retention: 90 days hot, 3 years cold
  - Fault tolerance: no data loss

Proposed architecture:

Producers -> Kafka (partitioned by service) -+-> Flink (real-time agg)
                                             |     -> Redis (hot counters)
                                             +-> S3 (raw parquet, partitioned by date)
                                                  -> Spark (batch anomaly, daily)
                                                  -> Trino/Presto (ad-hoc SQL)
                                             Dashboard -> Druid (pre-agg OLAP)

Follow-ups

Why partition Kafka by service rather than by timestamp? What are the tradeoffs?
How do you handle late-arriving events in the Flink streaming layer?
What compaction and partitioning strategy on S3 enables fast Trino queries?
How would you implement exactly-once semantics end-to-end from Kafka to the database?

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Topics

Onsite System Design

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