Applied Intuition interview notes, page 7 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

Jul 2026

System design round at Applied Intuition for a software engineering role. One meaty question that took up the whole session, lots of follow-ups, and I left feeling like I'd only scratched the surface of what they actually wanted.

You have a massive POSIX-like filesystem with millions of files in nested directories. Design an algorithm to find groups of duplicate files without reading file contents. You can only use metadata like file size, timestamps, permissions, and inode info. Cover how you'd traverse the tree, avoid symlink loops, handle hard links, deal with permission errors, and define what 'duplicate' even means under these constraints. Also discuss data structures, memory and I/O tradeoffs for scale, and give complexity analysis.

“This one wrecked me a little.”

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Applied Intuition·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Applied ML engineer interview with a pretty deep CUDA question about deterministic floating-point reductions. The whole thing felt more like a systems/numerics research chat than a standard coding screen, which I wasn't fully prepared for.

How would you design a deterministic parallel reduction in CUDA that guarantees a fixed combination order, given that floating-point addition is not perfectly associative?
How do you combine reduction results across CUDA blocks without using nondeterministic atomic operations?
What guarantees can and cannot be made about floating-point reductions, even with a deterministic ordering scheme?
What are the time and memory tradeoffs of a deterministic reduction approach compared to a standard parallel reduction?

“The interviewer nudged me toward thinking about prefix sums, which helped a lot.”

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Applied Intuition·Machine Learning Engineer·Take-home Assignment

Jul 2026

Applied Intuition ML engineer interview centered on a take-home Colab notebook with a broken GPT-style model. The task was to find and fix three specific bugs, write tests, and explain the reasoning. Pretty hands-on, no fluff.

Given a minimal GPT-style language model in a Colab notebook, identify and fix three bugs: a broken attention mask, a training loop error, and missing positional encoding. Also write unit tests that would have caught each bug and explain your reasoning.

“This took me longer than I expected on the attention masking piece.”

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Applied Intuition·Software Engineer·Technical Phone Screen

Jul 2026

Applied gave me a coding round that was basically a mini database engine. One problem, but it had enough edge cases to keep me busy for the whole session.

Build an in-memory key-value store that supports SET, GET, DELETE, and nested transactions with COMMIT and ROLLBACK semantics.

“My first instinct was a stack of hashmaps, one per transaction level.”

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