Company
Applied Intuition interview questions
48 practice questions in the style of Applied Intuition's interviews, covering Software Engineering Fundamentals, Coding & Algorithms, ML System Design, System Design, Behavioral & Leadership, Data Manipulation (SQL/Python), Machine Learning.
4 easy34 medium10 hard
Applied Intuition interview questions by the numbers
Last updated
Katkat has 48 Applied Intuition interview questions. The largest topics are Coding & Algorithms (52%), Software Engineering Fundamentals (23%) and System Design (13%). By difficulty: 8% easy, 71% medium and 21% hard. The most common round is Technical Screen (69%). Frequent concepts include Data Structures, Algorithms, System Design and Concurrency.
Topics
- Coding & Algorithms52% · 25
- System Design13% · 6
- Behavioral & Leadership6% · 3
- Machine Learning2% · 1
- ML System Design2% · 1
Difficulty
- Easy8% · 4
- Medium71% · 34
- Hard21% · 10
Interview rounds
- Technical Screen69% · 33
- Onsite31% · 15
Roles
- Software Engineer88% · 42
- Machine Learning Engineer10% · 5
- Backend Engineer2% · 1
Frequent concepts
- Data Structures15% · 7
- Algorithms10% · 5
- System Design8% · 4
- Concurrency6% · 3
- Geometry6% · 3
- geometry4% · 2
Most mentioned in interview experiences
- 1.Design a transactional in-memory key–value store2 reports
- 2.Mission motivation and a walkthrough of your school, major and job decisions2 reports
- 3.Track simulations and report timeouts2 reports
- 4.Build an Ingestion Service and Timestamp-Indexed Query API for MCAP Driving Logs1 report
- 5.Build and present a minimal vehicle log playback app from S3 data in two hours1 report
- 6.Design a nested transaction store1 report
- 7.Design a real-time 3D playback system for recorded vehicle logs1 report
- 8.Design Parsing Boundaries for Interdependent Message Types1 report
- 9.Discuss productionizing event-timeout detector1 report
- 10.Evaluate Dependent Variable Assignments1 report
Counted from 10 candidate interview experiences on Katkat. How we collect and count
Showing 1–30 of 48 results
A hands-on build exercise over recorded vehicle drive logs stored in MCAP, with Protobuf-encoded camera and location messages. The candidate ingests the runs, designs storage and a time index, exposes APIs to list runs and query messages by timestamp, then demos the result and explains how they worked with an AI coding assistant.
Applied IntuitionSoftware EngineerSoftware Engineering FundamentalsOnsiteGiven two hours, an AI coding assistant and an S3 bucket of unfamiliar vehicle log data, build a minimal 3D log playback app and then present it in 30 minutes. The exercise tests data exploration, tight scoping, verifying AI-generated code, and explaining trade-offs and future work clearly.
Applied IntuitionSoftware EngineerSoftware Engineering FundamentalsOnsiteThis question tests the ability to efficiently identify unique elements in a sequence while maintaining their original relative order, mapping them to integer indices to compress a large dataset.
Applied IntuitionSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates debugging and implementation skills for transformer-based training pipelines, specifically attention masking, training-loop correctness, positional encoding integration, and the ability to design unit tests that catch such bugs.
Applied IntuitionMachine Learning EngineerML System DesignTechnical ScreenThis question evaluates understanding of parallel GPU programming, deterministic reduction ordering, and numerical stability in floating-point arithmetic, within the domain of parallel algorithms, GPU/CUDA programming, and numerical computing for a Software Engineering Fundamentals / Machine Learning Engineer role.
Applied IntuitionMachine Learning EngineerSoftware Engineering FundamentalsTechnical ScreenDesign parsing and type-resolution boundaries for interdependent message definitions, clarifying schema versus encoded input, forward references, recursion, and validation.
Applied IntuitionSoftware EngineerSoftware Engineering FundamentalsTechnical ScreenDesign an end-to-end ingestion, validation, and storage pipeline for high-frequency sensor telemetry from autonomous vehicle fleets. The focus is on handling raw versus derived data, schema modeling, fast time-range queries, and robust validation.
Applied IntuitionSoftware EngineerSystem DesignTechnical ScreenThis problem tests your understanding of multi-threaded synchronization primitives, memory ordering, and circular buffer logic in a low-latency environment.
Applied IntuitionSoftware EngineerSoftware Engineering FundamentalsTechnical ScreenImplement an in-memory reassembler in C that reconstructs out-of-order chunked TLV messages, validates chunk bounds and duplicates, and detects message completion.
Applied IntuitionSoftware EngineerSoftware Engineering FundamentalsTechnical ScreenThis problem tests the ability to design an in-memory key-value store that supports nested transactions, including atomic commits, rollbacks, and precise state management across layers.
Applied IntuitionSoftware EngineerSoftware Engineering FundamentalsTechnical ScreenThis question evaluates competency in designing scalable, durable distributed job schedulers, covering multi-tenant architecture, queuing and metadata persistence, execution semantics, idempotency, retries/dead-lettering, failure handling, and observability.
Applied IntuitionSoftware EngineerSystem DesignOnsiteThis question evaluates understanding of language design and implementation skills, including lexical analysis, parsing into an AST, expression evaluation with operator precedence and unary operators, variable assignment semantics, and runtime error reporting.
Applied IntuitionSoftware EngineerSoftware Engineering FundamentalsOnsiteThis question evaluates understanding of transactional state management, nested transaction semantics, scope resolution, and data-structure design for an in-memory key–value store.
Applied IntuitionSoftware EngineerCoding & AlgorithmsOnsiteDesign a system that plays recorded vehicle log data back as a smooth, seekable 3D scene without downloading the whole log first. It tests chunked, time-indexed streaming, prefetching and caching around the playhead, aligning data streams of different rates, and handling heavy sensor data at scale.
Applied IntuitionSoftware EngineerSystem DesignOnsiteThis question assesses the ability to architect a high-throughput simulation system capable of retrieving, syncing, and replaying synchronized sensor data streams for autonomous vehicle testing.
Applied IntuitionSoftware EngineerSystem DesignOnsiteThis question evaluates understanding of transactional state management, nested transaction semantics, scope shadowing, and efficient data-structure design for an in-memory key–value store, assessing competency in correctness, consistency, and algorithmic complexity under nested BEGIN/ROLLBACK/COMMIT operations.
Applied IntuitionSoftware EngineerCoding & AlgorithmsTechnical ScreenDiscuss productionizing event-timeout detector evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Applied IntuitionSoftware EngineerSystem DesignTechnical ScreenEvaluate a list of variable assignment statements where variable values can depend on variables defined later in the input. The problem tests expression parsing, cycle detection, dependency resolution via topological ordering or memoized depth-first search, and deep recursion handling.
Applied IntuitionSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates implementation skills in parsing and evaluating a small arithmetic DSL, covering variable assignment parsing, symbol resolution, and evaluation order; it falls under the Coding & Algorithms category and emphasizes practical application and engineering of a correct evaluator rather than abstract theory.
Applied IntuitionSoftware EngineerCoding & AlgorithmsTechnical ScreenThis interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Explain motivation and background states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Applied IntuitionSoftware EngineerBehavioral & LeadershipTechnical ScreenThis question evaluates a candidate's self-assessment, accountability, communication, and early risk-identification competencies in the context of individual performance and team dynamics for a Software Engineer role within the Behavioral & Leadership domain.
Applied IntuitionSoftware EngineerBehavioral & LeadershipOnsiteThis question evaluates file parsing, data deduplication and grouping skills along with algorithmic efficiency and space-time trade-offs when identifying duplicate files by size rather than by content.
Applied IntuitionSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question tests the ability to modify the standard binary search algorithm to achieve logarithmic time complexity when searching for the boundary indices of a target value in a sorted array.
Applied IntuitionSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates proficiency in grid-based graph traversal and shortest-path computation using BFS, reachability analysis from multiple marked points, and algorithmic time/space complexity reasoning.
Applied IntuitionSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates a candidate's skills in computational geometry and numerical robustness, focusing on line-segment intersection reasoning, endpoint touching, collinearity, and precision handling.
Applied IntuitionSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates understanding of file deduplication, efficient I/O and hashing strategies, streaming large-file processing, and scalable algorithm design for identifying files with identical byte-for-byte contents.
Applied IntuitionSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates proficiency with double-ended queue semantics, fixed-capacity data structure design, constant-time (O(1)) operations, and correct handling of boundary conditions and wraparound.
Applied IntuitionMachine Learning EngineerCoding & AlgorithmsTechnical ScreenThis question tests the ability to implement a space-efficient data structure using fixed-size arrays, modular arithmetic for index wrapping, and generic type constraints.
Applied IntuitionSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates proficiency in nested data validation, schema interpretation, and type checking for hierarchical objects, including handling nulls, arrays, extra fields, and clear error reporting.
Applied IntuitionSoftware EngineerData Manipulation (SQL/Python)OnsiteThis problem tests your ability to design a priority-based scheduler using a min-heap to manage tasks triggered by relative or absolute timestamps, ensuring execution occurs in the correct temporal order.
Applied IntuitionSoftware EngineerCoding & AlgorithmsOnsite
Applied Intuition interview experiences
All 11 →- 45-Minute Phone Screen With Merge SegmentsApplied Intuition · Software Engineer · Technical Screen · Oct 2026
Old question: merge segments. You also have to think about how to handle slope 0. Solution: a math problem plus merge intervals. Now let me complain about this company's interview. The phone screen was 45 minutes in total: 5 min intro + 5 min Q&A, so that leaves 35 min for the problem, and you still have to communicate the whole time. 🙄 Even if I had seen it before and practiced in Java, I'd be d…
- AI-Assisted Virtual Onsite Building a Query API over MCAP Driving LogsApplied Intuition · Software Engineer · Onsite · Oct 2026Rejected
This is the same as the earlier thread about this interview. The format is MCAP, and the content is car driving data, like video, images, location information... folder structure of the logs: MCAP defines a message schema, and the message content is ProtoBuf encoded. Every message has a timestamp, and the API I had to implement needs to query by timestamp, so an index is needed. My idea was to st…
- Game of Life Scaled to 1M x 1M in a Phone ScreenApplied Intuition · Software Engineer · Technical Screen · Oct 2026
The interviewer first demoed Conway's Game of Life in the browser (I had never heard of it before), then pasted starter code into CoderPad: next_gen computes the next generation of the board. There is a helper _process_cell(old_board, new_board, row, col) that decides whether a cell lives or dies based on its neighbor count. The initial board is random with a probability p between 0 and 1, so the…
- Real-Time 3D Playback Design, Then a 2-Hour AI-Assisted BuildApplied Intuition · Software Engineer · Onsite · Sep 2026
A 30-minute panel. It starts with a self-introduction, but the intro itself isn't the point. The main thing is that you need to explain why you're interested in the company's mission, so look up the mission statement on their website beforehand and talk about it. System design, 45 minutes. It was to build a real-time 3D playback system given logs of data. It's the toy-car modeling question that h…
- Aggressive Interruptions on a Coding Phone ScreenApplied Intuition · Software Engineer · Technical Screen · Aug 2026
HR call: Two different HR people reached out to me. It was basically background chat. The first HR felt like they came out of a frat, very flippant, and asked me this kind of dumb question about why I'd choose to accept a return offer and go back to a certain company as a full-time employee. The second one asked what I wanted out of my next job, and then asked to schedule the interview right ther…
More companies
- Meta666
- Amazon653
- Google486
- OpenAI342
- Capital One268
- Microsoft252
- DoorDash241
- Anthropic225
- Uber217
- Apple177
- TikTok176
- Pinterest161
- ByteDance160
- LinkedIn154
- Stripe139
- Snowflake130
- Databricks122
- Airbnb112
- Rippling106
- Oracle104
- Coinbase99
- Netflix97
- NVIDIA95
- Snapchat94
- Citadel90
- Waymo90
- Instacart87
- Roblox84
- Salesforce77
- Optiver76
- Shopify63
- Robinhood60
- Atlassian55
- Hudson River Trading55
- Two Sigma55
- J.P. Morgan53
- PayPal53
- xAI53
- Lyft52
- Tesla52
