UiPath interview questions

Showing 1–13 of 13 results

• Build an Event-Driven Folder BrowserEasy

  This problem tests your ability to model hierarchical data structures and design an efficient state management strategy for an interactive UI component.

  UiPathFrontend EngineerSoftware Engineering FundamentalsOnsite
• Compute Minimum L-Moves on Infinite GridMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute Minimum L-Moves on Infinite Grid states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  UiPathMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Compute longest distinct substring, case-insensitiveMedium

  This question evaluates competency in string processing, character normalization for case-insensitivity, algorithmic complexity analysis, and handling large-scale or streaming inputs.

  UiPathSoftware EngineerCoding & AlgorithmsTechnical Screen
• Describe project scope and ownershipMedium

  This question evaluates ownership and technical leadership, including end-to-end project execution, architectural and product decision-making, trade-off analysis, and the ability to quantify impact under constraints.

  UiPathSoftware EngineerBehavioral & LeadershipTechnical Screen
• Explain Core ML FundamentalsEasy

  This question tests your conceptual understanding of how fundamental components of neural networks and predictive models affect optimization, generalization, and performance.

  UiPathMachine Learning EngineerMachine LearningTechnical Screen
• Explain Transformer Layers and FFN RationaleMedium

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain Transformer Layers and FFN Rationale states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  UiPathMachine Learning EngineerMachine LearningTechnical Screen
• Explain database design and consistencyHard

  This question evaluates proficiency in database design and consistency concepts, covering competencies such as data modeling, datastore selection, partitioning and indexing strategies, consistency semantics, operational trade-offs, validation and testing, and privacy/compliance considerations.

  UiPathSoftware EngineerSystem DesignTechnical Screen
• Implement cocktail shaker sort and analyzeMedium

  This question evaluates a candidate's understanding of sorting algorithms and algorithmic optimization, specifically implementation and enhancement of cocktail shaker sort, plus analysis of time and space complexity, stability, and counts of comparisons and swaps.

  UiPathMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• LLM fundamentals: Transformer internals, beam search decoding, and LoRA fine-tuningMedium

  An LLM fundamentals question that asks you to explain Transformer internals such as scaled self-attention, multi-head attention, positional information and causal masking. It also covers how beam search decoding compares with greedy decoding and sampling, and how LoRA reduces the cost of fine-tuning.

  UiPathMachine Learning EngineerMachine LearningTechnical Screen
• Probability a draw-until-threshold score ends at or below a limitMedium

  A coding question on probability: a player repeatedly adds a uniformly random number of points until the total reaches a threshold, and you return the probability that the final total is at most a given limit. It tests careful probabilistic reasoning and an efficient computation for inputs up to ten thousand.

  UiPathMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Reduce overfitting and explain the mean squared error lossMedium

  A machine learning phone screen on how to diagnose and reduce overfitting, and on the mean squared error loss: its definition, what minimizing it estimates, its probabilistic interpretation, and when another loss works better. Tests fundamentals, mechanisms and trade-off reasoning.

  UiPathMachine Learning EngineerMachine LearningHR Screen
• Reliable LLM tool calling: format control, failure recovery, and runaway loopsMedium

  An LLM agent question on tool calling: how a model invokes tools, how to keep tool calls in a valid format, how to make tool use stable and fix wrong calls, and how to handle tool-call loops that run too long. It tests practical reliability engineering for production agents.

  UiPathMachine Learning EngineerMachine LearningTechnical Screen
• Walk through resume and handle ambiguityMedium

  This question evaluates a candidate's ability to communicate technical career history, quantify project impact, and demonstrate leadership and ambiguity-handling skills relevant to a Machine Learning Engineer role.

  UiPathMachine Learning EngineerBehavioral & LeadershipTechnical Screen