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Applied Intuition interview notes, page 1 of 7

Mid-sized

Most of these Applied Intuition write-ups come from Software Engineers (50), then Machine Learning Engineers (12) and Mobile Engineers (2). The most reported rounds are the technical phone screen (42), the system design round (14), the hiring manager screen (4) and the recruiter screen (2).

64 interview notes · updated Jul 2026

Applied Intuition·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Applied Intuition system design round for a software engineering role. The whole session was basically one big deep-dive into building a transactional key-value store from scratch, which sounds contained until you realize how many edge cases they want you to think through.
  • Design and implement an in-memory key-value store that supports transactions, including nested transactions, with get, set, delete, begin, commit, and rollback operations.

“I started with the basic get/set/delete part, which felt fine, then they said 'now add transactions' and I kind of froze for a second.”

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Applied Intuition·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Applied Intuition system design round focused entirely on building a replay simulator for autonomous vehicle evaluation. Pretty niche problem space but the depth they expected was real, covering everything from data modeling to API shape to storage architecture.
  • Design a simulator that can replay recorded driving scenarios (specifically left-turn intersections) for autonomous vehicle regression testing. Walk through the full system.
  • How would you design the data schema to represent a driving scenario, including sensor data, ego-vehicle state, surrounding agents, and map information?
  • What API surface would you expose for this simulator? Think about searching scenarios, fetching a replay, scrubbing to a specific timestamp, and branching a replay with edits.
  • What storage architecture would you use for this system, given that you have raw sensor data, time-series telemetry, and scenario metadata with different access patterns?

“This was the whole interview, basically.”

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Applied Intuition·Software Engineer·Technical Phone Screen

Apr 2026
Applied Intuition SWE interview with a schema validation problem that's less algorithm-heavy and more about careful parsing logic. The problem itself isn't hard conceptually but there are enough edge cases to trip you up if you're not methodical.
  • Given a schema that describes fields with types and optional/required flags (similar to protobuf), write a function that validates whether a JSON document conforms to that schema, including handling nested message types and rejecting unknown fields in strict mode.

“The recursion part is where I fumbled a bit.”

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Applied Intuition·Machine Learning Engineer·Hiring Manager Screen

Apr 2026
Hiring manager screen for an ML Engineer role at Applied Intuition. Started pretty casual with background questions, then got into expectations pretty quickly. Nothing too technical from what I could tell, but the 50 hours a week comment stuck with me.
  • Walk me through your educational background and what you studied.

“Pretty standard opener.”

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Applied Intuition·Software Engineer·Technical Phone Screen

Apr 2026
Applied Intuition had me build a mini compiler from scratch during what felt like a very long technical screen. The scope was broader than I expected and covered way more ground than a typical coding question.
  • Implement a mini compiler or interpreter for a small language, covering tokenization, parsing into an AST, and evaluation of constructs like variable assignment, arithmetic, conditionals, and possibly function calls. Also discuss your grammar design, error handling strategy, and how you'd extend the language.

“This was a lot.”

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Applied Intuition·Software Engineer·Technical Phone Screen

Apr 2026
Applied Intuition coding screen for a software engineer role. One question, but it had enough moving parts to keep you busy for a while.
  • Given a message definition string where each line specifies a field name and its type, implement a parser that supports two operations: returning the size in bytes for a given field name, primitive type, or user-defined message type; and returning the type string for a given field name.

“The setup sounds manageable until you realize get_size has to handle three different kinds of inputs and you need to figure out which one you're dealing with at query time.”

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Applied Intuition·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Applied Intuition ML Engineer interview. The only technical content I can pin down is a LeetCode problem that got referenced, so not a ton to go on here.
  • Solve a LeetCode-style coding problem (algorithmic, referenced internally by a coded name).

“The problem itself wasn't described in detail so I can't tell you exactly what the solution looked like.”

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Applied Intuition·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Coding round at Applied Intuition for an MLE role. One problem, but it was a deceptively tricky config validation thing that required a lot of back-and-forth with the interviewer just to understand the scope.
  • Implement a config validator that takes a set of descriptors (potentially nested, e.g. a PersonDescriptor with name, email, and an AddressDescriptor) and validates a JSON input against them.

“The problem sounds manageable until you realize all the descriptor classes are custom and you have to figure out the contract yourself.”

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Applied Intuition·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Applied Intuition MLE interview threw a curveball with a CUDA-specific question I had zero prep for. Short session but it left me thinking I need to go deeper on GPU parallelism fundamentals.
  • Describe a CUDA algorithm that guarantees a specific reduction order, similar in concept to prefix sum.

“Had no idea.”

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Applied Intuition·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at Applied Intuition focused entirely on building a production-grade event-timeout detector. It was one of those sessions where the scope kept expanding and I kept realizing how many angles I hadn't thought through.
  • Design a production-ready event-timeout detector. Walk through the external API, data model including schemas and idempotency keys, and how you handle time semantics like processing time versus event time and clock skew.
  • How would you shard this system by event_id, what state storage would you use, what consistency model do you need for reads, and how do you handle failure recovery?
  • Compare timer wheels versus heap-based or LRU-based approaches for managing timeouts at scale. How do batching and backpressure factor in, and how do you enforce memory limits?
  • What production edge cases does this system need to handle, specifically late, dropped, duplicated, or reordered messages, very large timeout windows, long silent periods, and restarts? How would you test, monitor, and alert on all of this?

“I started with the API surface and felt okay there, but idempotency keys tripped me up a bit.”

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