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Luma AI interview notes, page 1 of 2

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Screna has 12 Luma AI interview write-ups. The most reported round is the technical phone screen (12).

12 interview notes · updated Jul 2026

Luma AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Coding round for an MLE role at Luma AI. One algorithmic problem, classic computational geometry, but the scale of the input made the naive approach a non-starter.
  • Given up to 20,000 points on a 2D plane, find the minimum Euclidean distance between any pair of points.

“I knew the brute force immediately, two nested loops, done.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Luma AI ML engineer interview with a meaty debugging question about a broken Colab training setup. One question, but it covered a lot of ground simultaneously, felt more like a take-home style problem delivered verbally.
  • You're given a Colab notebook that trains a denoising neural network on MNIST but the training loss isn't decreasing and the eval loss is way off. How do you debug and fix it? Specifically: there's a train/test data distribution mismatch, a bad activation function at the output layer, and the optimizer update loop is broken.

“This one had three distinct failure modes packed into a single scenario and they wanted you to walk through all of them.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML engineer role at Luma AI and got hit with a low-level systems question that felt pretty out of left field for the position.
  • Implement an image copy operation from scratch, without using any external libraries.

“Not what I was expecting from an ML interview.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

May 2026
Coding round for an MLE role at Luma AI. Just one algorithmic problem, pretty focused on array manipulation and geometry basics.
  • Given an array of positive integers, return a binary array of length n-2 where each element indicates whether the corresponding triple of consecutive values can form a valid triangle.

“The triangle inequality part is straightforward once you remember you need all three checks, not just one.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an MLE role at Luma AI and got a coding question that was a spin on a classic matrix problem. Not much context given about the round or outcome.
  • Given a 2D image represented as a matrix, write a function to transpose it. Note: this is a variant of the standard problem, not the exact LeetCode version.

“I've done the LeetCode transpose before so my first instinct was to just write that solution.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Luma AI, got a computational geometry problem that felt more like a CS fundamentals check than anything ML-specific.
  • Given a set of points, find the minimum distance between any two of them.

“Brute force is O(n^2) and I knew that wasn't the answer they wanted.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Luma AI and got a fairly math-heavy coding question about image processing. Not the typical leetcode grind, which was a nice change, but it definitely required you to actually know the underlying math.
  • Implement a k x k 2D Gaussian blur kernel from scratch using the Gaussian formula and a given sigma value. Distances from the center should be used for x and y, and the final kernel must be normalized so all values sum to 1.

“I knew the formula but fumbled the normalization part at first.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Luma AI ML engineer interview had a coding question that looks trivial until you actually think about it. The numerical stability angle is what separates a passing answer from a failing one.
  • Implement softmax on a list of float logits from scratch, no external libraries except math.exp. Make sure it handles numerical overflow correctly.

“My first pass just exponentiated everything directly and called it done.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Luma AI ML engineer screen, one coding problem focused on data preprocessing. Pretty straightforward if you know numpy/pandas, but the combination of imputation and standardization in one clean pipeline is easy to fumble if you're not careful about the order of operations.
  • Given a matrix of positive floats where zeros indicate missing values, impute the zeros using the per-column mean of non-zero entries, then standardize each column to have mean 0 and standard deviation 1. You can use numpy, pandas, or sklearn.

“The imputation part is fine, masking zeros and computing column means is a few lines.”

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Luma AI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML Engineer role at Luma AI and got asked to implement softmax from scratch. Pretty standard for this type of role but still worth thinking through carefully.
  • Implement the softmax function given its formula.

“They handed over the formula so it wasn't a recall test, more about whether you could translate math into clean code.”

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