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Dandy

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Screna has 10 Dandy interview write-ups. The most reported round is the technical phone screen (10).

10 interview notes · updated Jul 2026

Dandy·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Dandy ML Engineer interview had at least one deep technical question that pushed into territory I wasn't fully prepared for. The kind of question where you know the pieces but assembling them under pressure is a different story.
  • Design a neural network that takes a list of floating-point numbers as input and outputs the sorted version. Walk through how you'd represent the input, formulate the output, handle differentiability, set up training, and where this approach breaks down compared to just using a classical sort.

“This one had a lot of layers and I think I handled the first half okay.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML Engineer role at Dandy and got a math-heavy technical question that went deeper than I expected. Not a vibe-check round, they actually wanted to talk through edge cases.
  • Given two 3D points, compute the Euclidean distance between them. Then discuss numerical stability issues and how you'd extend the formula to N dimensions.

“Started fine, wrote out the formula no problem.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML Engineer role at Dandy and got a pretty meaty stats/ML theory question that I wasn't fully prepared to articulate cleanly under pressure.
  • Compare leave-one-out cross-validation and k-fold cross-validation (say, 5-fold) as estimators of test error. Walk through the bias-variance tradeoff between them and discuss computational cost. Which would you actually use in practice?

“I knew the broad strokes but fumbled the variance explanation a bit.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML Engineer role at Dandy and got a math-heavy question about 3D interpolation that felt more like a graphics or geometry course exam than anything I expected going into this.
  • Given two points in 3D space, how do you compute an interpolated point between them? Walk through the parameter definition, the formula, and what it means geometrically. Also cover what happens if the parameter goes outside the normal range.

“I knew the formula but fumbled the explanation of what the parameter actually means geometrically.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

May 2026
Technical screen for an ML Engineer role at Dandy. One question, pretty deep on neural network fundamentals. Left feeling like I either nailed it or completely missed what they were actually probing for.
  • If you replace every activation function in a deep neural network with the identity function y = x, what happens to the network's expressive power? Walk through why depth stops mattering, why non-linear functions become unrepresentable, and when you'd actually want identity activations on purpose.

“I knew the core answer: stacking linear layers just collapses to a single linear transformation because composing affine maps gives you another affine map.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

May 2026
ML engineer screen at Dandy with a meaty computer vision problem about height estimation from photos. Single question but they really wanted to see you sweat through the design choices.
  • You have a photo with multiple people in it. The camera's focal length and each person's distance from the camera are unknown. Design an ML system to estimate each person's real-world height.

“This one is deceptively open-ended.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML Engineer role at Dandy. The technical screen was pretty NLP-heavy and basically turned into a deep dive on one project from my resume. They wanted specifics, like actual model names and version numbers, which tripped me up a bit.
  • Walk me through an NLP project from your resume. Cover the problem, your preprocessing pipeline, the exact model you used and why, your training setup, and what results you got. What would you do differently now?

“This felt like one question but it was really five questions stapled together.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Interviewed for an ML Engineer role at Dandy and got a geometry/math question that felt more like a graphics or robotics screen than anything ML-specific. Not what I was expecting.
  • Given two points in 3D space, write a function to return the linearly interpolated point at a parameter t between 0 and 1. Then discuss how you'd extend this to a polyline of N points, handle t values outside the valid range, and account for numerical precision issues.

“The basic lerp part was fine, P(t) = (1-t)*P0 + t*P1, done in like two minutes.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

Apr 2026
ML engineer interview at Dandy, one technical question that went pretty deep into transformer architecture and scaling limitations. Not a coding round, more of a conceptual deep-dive where they wanted you to connect theory to real engineering tradeoffs.
  • In a transformer-based model, what makes very long input contexts difficult to handle? Walk through the challenges across computation, memory, attention behavior, and inference efficiency, and touch on how people typically address them.

“Broader than it sounds.”

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Dandy·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Had a technical screen for an ML Engineer role at Dandy that went pretty deep into Transformer internals, specifically around long-context limitations. One meaty question that took up most of the time.
  • Why is processing very long sequences difficult for standard Transformers, and what approaches exist to address it?

“This question sprawled in a way I didn't fully anticipate.”

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