Capital One Machine Learning Engineer Interview Questions

Capital One·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Interviewed for an ML engineer role at Capital One and got a coding question about string parsing. Pretty standard technical screen, nothing too wild.

How would you check whether the parentheses in a given string are balanced, using Python?

“Classic stack problem.”

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Capital One·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Capital One ML Engineer interview that was heavier on algorithms than I expected. Two coding problems, both with real complexity requirements attached, so it wasn't just "write the code" but also justify the design choices and handle edge cases out loud.

Given an n x n integer matrix, write a function that returns a new matrix rotated 90 degrees clockwise. Extra space is fine. Then explain how you'd do the same rotation in-place, and walk through the time and space trade-offs between the two approaches. Cover edge cases like 1x1, 2x2, odd vs even n, and negative values.
You have N lamps on a number line, each covering a closed interval around its position. Find the maximum number of lamps that illuminate any single point, then output all merged segments where that maximum coverage occurs, sorted by coordinate. N can be up to 200,000 and coordinates can be up to 1 billion in absolute value. Design an O(N log N) solution and justify correctness and complexity. Also discuss how the answer changes if intervals are open instead of closed, or if you restrict to integer coordinates.

“Started with the extra-space version which is pretty mechanical, just map (i, j) to (j, n-1-i) in a new matrix.”

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Capital One·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Capital One ML Engineer interview focused heavily on productionization, which I wasn't fully expecting. Three meaty questions back to back on deployment, monitoring, and a latency debugging scenario. No fluff, no warmup.

Walk me through how you would deploy an ML model end to end, including packaging, serving infrastructure, CI/CD, and keeping training and serving features in sync.
How do you monitor an ML model once it's live? What signals would you track across system health, data quality, model behavior, and business outcomes?
A fraud detection model served via REST API keeps missing a sub-50ms p99 latency target. How do you diagnose the bottleneck and fix it?

“This one sprawls if you let it.”

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Capital One·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Capital One ML Engineer interview with a coding question that was pretty light on the ML side. Just a basic string manipulation problem, nothing that really tested modeling knowledge.

Given a width, height, and optional fill character, write a function that prints a rectangular picture frame as a multi-line string, with border characters forming the rectangle and the interior either blank or filled.

“Pretty basic loop problem.”

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Capital One·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Capital One ML Engineer interview with a coding problem that looked straightforward on the surface but had a few edge cases that slowed me down more than I'd like to admit.

Given n servers each with a remaining capacity and a shutdown flag, simulate a round-robin package assignment process where each incoming package is assigned to the next eligible server, skipping any that are shut down or at capacity. After all packages are processed, return the highest index among servers that handled the most packages.

“I got the basic loop going pretty fast but tripped on the wrap-around logic.”

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Capital One·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Capital One ML Engineer loop, all conceptual, no coding. Six questions covering the full ML stack from theory to system design to a personal project walkthrough. Pretty thorough for a single session and felt more like a staff-level screen than a mid-level one.

Can you explain the bias-variance tradeoff, how it connects to underfitting and overfitting, and what practical levers you'd use to move along it?
What is the difference between parameters and hyperparameters? Give examples of each and explain how each gets set.
Compare batch inference and real-time inference across latency, throughput, cost, and use cases. How do the feature computation and serving architecture differ between them?
If your training data is too large to fit on a single machine, what strategies would you use to handle it?
What is feature engineering and why does it still matter even when using modern deep learning or large pretrained models?
Walk me through the end-to-end ML lifecycle from problem framing to deployment and monitoring. Then describe a specific project you personally delivered, covering the problem, data, modeling, evaluation, deployment, and how you iterated after launch.

“Knew this cold.”

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Capital One·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026

Capital One ML Engineer system design round, focused entirely on building a rule engine for a virtual credit card validation service. Pretty interesting problem space but the scope kept expanding as the interview went on.

Design a rule engine API for validating virtual credit card transactions. The API should accept a transaction and a list of rules, and return whether the transaction is valid along with any rules that were violated.

“This one sprawled fast.”

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Capital One·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026

Three behavioral questions for an ML Engineer role at Capital One. Nothing technical, just back-to-back stories about handling hard situations. Felt a bit repetitive by the third one but I got through it.

Tell me about a time you faced a significant challenge. What made it hard, and how did you push through?
Describe a situation where you were blocked on something but still managed to deliver on time. How did you get yourself unblocked?
Tell me about something you accomplished that most of your peers struggled with. What gave you the edge?

“This is the warmup question that somehow still trips people up.”

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Capital One·Machine Learning Engineer·Online Assessment (OA)

Jun 2026

Capital One ML Engineer OA, four independent coding problems with no behavioral component. The problems ranged from pretty approachable to genuinely annoying to implement correctly, especially that second one with the repeated subtraction logic.

Given an array of daily website visit counts and a target number, return the earliest day index where the cumulative visits reach or exceed that target.
Simulate a repeated subtraction process on an array: repeatedly find the first positive value, subtract it from all following elements until you hit something smaller, accumulate it into a result, and repeat until no positives remain.
Place a series of labeled blocks with fixed shapes onto a grid, choosing the topmost then leftmost valid position for each, and return the final grid state.
Given an array of house heights where you can only increase values, find the minimum number of increments to make the heights form either a strictly increasing or strictly decreasing sequence with step size exactly 1.

“Straightforward prefix sum scan.”

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Capital One·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Capital One ML Engineer interview with two coding problems. The grid one was genuinely tricky and I'm not sure I fully nailed it, but the second felt more approachable once I saw the pattern.

Given an m x n grid where each cell is either a digit or an operator ('+' or '-'), find the maximum value achievable by any path from the top-left to the bottom-right (moving only right or down) such that the concatenated cells form a valid alternating digit-operator expression evaluated left to right.
Given an integer array, count the total number of contiguous subarrays where adjacent elements always alternate between even and odd (sawtooth subarrays), with single-element subarrays counting as valid.

“This one took me a while to even parse correctly.”

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