UiPath Machine Learning Engineer Interview Questions

UiPath·Machine Learning Engineer·Technical Phone Screen

Jul 2026

UiPath ML Engineer interview, technical phone screen focused on ML fundamentals. The loss function question was the centerpiece and went deeper than I expected.

What loss function would you use for a regression model, and how do the common options compare in terms of robustness, gradient behavior, and noise assumptions?

“I started with MSE and MAE which was fine, but the follow-up about noise assumptions is where I got a bit shaky.”

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UiPath·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Interviewed for an ML Engineer role at UiPath and got a probability question that felt more like a stats refresher than anything ML-specific. Pretty straightforward if you remember your independence rules.

You roll two fair six-sided dice twice (four total die rolls). What is the probability that all four outcomes are even?

“Each die has a 1/2 shot at landing even, and since the rolls don't affect each other, you just multiply (1/2) four times and get 1/16.”

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UiPath·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Got a coding round at UiPath for an ML Engineer role and they threw a pure algorithms problem at me, which I wasn't really expecting. It was a knight-moves-on-infinite-grid variant and the scale of the coordinates meant BFS alone would've been a disaster.

On an infinite 2D grid, a piece moves in L-shapes (like a chess knight). Given a target coordinate where each axis can be up to 1 billion in magnitude, write a function that returns the minimum number of moves to reach that target from the origin. You must exploit symmetry, handle edge cases, and explain your algorithm with complexity analysis.

“My first instinct was BFS and I even started writing it out before realizing coordinates go up to 1e9.”

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UiPath·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Interviewed for an ML Engineer role at UiPath and got a coding question that felt more like a software engineering screen than anything ML-related. Classic string problem, but they wanted me to walk through multiple approaches and complexity tradeoffs, which I wasn't fully prepared for.

Given an array of strings, write a function that finds the longest common prefix shared by all strings. Return an empty string if none exists.

“I jumped straight to the vertical scan approach (compare character by character across all strings at the same index) which works fine, but then they asked me to walk through other ways to do it.”

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UiPath·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at UiPath for an ML Engineer role, basically one big ambiguity question dressed up with a lot of sub-parts.

Tell me about a time when project requirements were unclear or kept shifting. How did you figure out what success looked like, who the right stakeholders were, and how to reduce uncertainty? What did you prioritize first, what trade-offs did you make, and how did you communicate those decisions?

“This question has like five questions inside it and I didn't realize that until I was already two minutes into my answer.”

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UiPath·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Interviewed for an ML Engineer role at UiPath, got a technical phone screen with some CS fundamentals thrown in. Nothing too wild but they did push on complexity analysis more than I expected.

What are the time complexities for insertion and deletion in a hashmap, and what factors affect them?

“I gave the O(1) average answer pretty quickly and thought I was done.”

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UiPath·Machine Learning Engineer·Technical Phone Screen

May 2026

Interviewed for an ML Engineer role at UiPath, technical phone screen focused on deep learning fundamentals. One question but it went pretty deep, they weren't satisfied with a surface-level answer.

Can you explain how dropout works in deep neural networks and why it's effective as a regularization technique?

“I started with the basic mechanic (randomly zeroing neuron outputs during training) and thought that'd be enough, but they kept pushing.”

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UiPath·Machine Learning Engineer·Technical Phone Screen

May 2026

Interviewed for an ML Engineer role at UiPath. The session mixed a resume walkthrough with a behavioral deep dive on ambiguity, which I wasn't fully prepared to tie together cleanly.

Walk me through your resume, focusing on the roles you've held, key projects, technologies you used, and any measurable impact you can point to.
Tell me about a specific situation where you faced significant ambiguity at work. What was unclear, how did you reduce that uncertainty, what trade-offs did you weigh, and what happened in the end?
What frameworks or habits do you rely on to manage ambiguity in ongoing projects, not just one-off situations?

“I rambled a bit on the earlier roles and spent too long on a project that wasn't even that relevant.”

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UiPath·Machine Learning Engineer·Technical Phone Screen

May 2026

Technical interview at UiPath for an ML Engineer role, focused almost entirely on Transformer internals. One long question that kept branching into sub-questions. Felt like a grad school oral exam more than a standard interview.

Walk through the Transformer architecture in full detail, covering both encoder and decoder layers, including multi-head attention, residual connections, layer normalization, and the feed-forward network. How do Pre-LN and Post-LN differ?
Why does a Transformer need a position-wise feed-forward network after the attention layer? What can the FFN do that attention alone cannot?
Using a concrete configuration like d_model=512 and 8 heads, trace the exact matrix shapes through a full attention block: Q/K/V projections, attention scores, softmax, weighted sum, head concatenation, output projection, residual addition, layer norm, and both FFN linear layers.
Derive the computational complexity of self-attention with respect to sequence length n and model dimension d. Where does memory become the bottleneck?
What are the trade-offs between different implementation choices in Transformers: fused vs. separate Q/K/V projections, multi-query and grouped-query attention, positional encoding schemes like RoPE or ALiBi, and FFN variants like SwiGLU or RMSNorm?
How do encoder and decoder layers structurally differ, and how do token representations change as they pass through successive layers in the stack?

“This is where the interview started and basically never left.”

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UiPath·Machine Learning Engineer·Technical Phone Screen

May 2026

Whiteboard-heavy technical screen for an ML Engineer role at UiPath. The whole session was basically a math notation audit, write the formula, get it exactly right, move on. Unforgiving if you're sloppy with summation indices or mix up scalar and vector notation.

Write the dot product formula for two vectors u and v in R^n from memory, with correct notation.
Write the cosine similarity formula between two vectors u and v, including the correct denominator.
Write the Euclidean (L2) distance and Manhattan (L1) distance formulas between two points.
Write the Mean Squared Error formula for n predictions versus targets, then derive the partial derivatives with respect to both the weight and bias parameters.
Write the min-max normalization formula and the z-score standardization formula.
As a bonus: write the sigmoid function and its derivative, the softmax function and its derivative, and binary cross-entropy.

“Seemed trivial going in.”

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