Katkat

Company

Figma interview questions

29 practice questions in the style of Figma's interviews, covering Behavioral & Leadership, Coding & Algorithms, Machine Learning, Analytics & Experimentation, System Design, ML System Design.

Questions29

0 easy24 medium5 hard

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Figma interview questions by the numbers

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Katkat has 29 Figma interview questions. The largest topics are Behavioral & Leadership (38%), System Design (24%) and Coding & Algorithms (21%). By difficulty: 83% medium and 17% hard. The most common round is Technical Screen (48%). Frequent concepts include System Design, Behavioral, Communication and Distributed Systems.

Difficulty

  • Medium83% · 24
  • Hard17% · 5

Interview rounds

  • Technical Screen48% · 14
  • Onsite38% · 11
  • HR Screen14% · 4

Roles

  • Software Engineer55% · 16
  • Data Scientist28% · 8
  • Machine Learning Engineer14% · 4
  • Data Engineer3% · 1

Frequent concepts

  • System Design17% · 5
  • Behavioral10% · 3
  • Communication10% · 3
  • Distributed Systems10% · 3
  • Stack10% · 3
  • Experimentation7% · 2

Showing 1–29 of 29 results

  • This question evaluates a candidate's behavioral and leadership competencies—specifically conflict resolution, influence without formal authority, learning from failure, and making trade-offs—while also testing their ability to quantify impact.

    FigmaSoftware EngineerBehavioral & LeadershipTechnical Screen
  • Practice graph reachability for simplified file-permission inheritance under directed, undirected, and blocked-edge variants. The prompt is useful for reviewing DFS or BFS, visited sets, cycle handling, and clarifying traversal semantics.

    FigmaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Implement and clarify the post-processing decoder for a BERT-based span-extraction model that maps token-level start and end logits back to non-overlapping character-level keyphrases.

    FigmaMachine Learning EngineerMachine LearningTechnical Screen
  • This question tests the ability to transform an abstract product objective into a rigorous measurement framework, focusing on metric selection and experimental design.

    FigmaData ScientistAnalytics & ExperimentationOnsite
  • Tests parsing a linear sequence of leveled tokens into a hierarchical tree or nested list structure using stack-based tree construction.

    FigmaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's ability to deliver a concise, metric-backed self-introduction demonstrating product-analytics impact, cross-functional influence, leadership presence, and alignment with a collaborative design SaaS mission.

    FigmaData ScientistBehavioral & LeadershipHR Screen
  • This question evaluates interpersonal communication, emotional intelligence, stakeholder management, and the ability to calibrate technical depth to an interviewer’s verbal and nonverbal cues.

    FigmaSoftware EngineerBehavioral & LeadershipHR Screen
  • Describe impact, prioritization, and stakeholder management evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    FigmaData ScientistBehavioral & LeadershipTechnical Screen
  • This question evaluates self-awareness, communication, motivation, collaboration, and role-alignment competencies that reveal which problem types and team environments enable a data scientist to perform best.

    FigmaData ScientistBehavioral & LeadershipTechnical Screen
  • Design a real-time, durable commenting system for an infinite collaborative whiteboard where comments attach to dynamic canvas objects or fixed coordinates.

    FigmaSoftware EngineerSystem DesignTechnical Screen
  • This problem tests the design of a dual-purpose ML system: a personalized content feed ranker and an LLM-based component that translates user intent into rendered HTML widgets.

    FigmaMachine Learning EngineerML System DesignOnsite
  • Design a permission-aware search and retrieval system for collaborative design files and assets that enforces hierarchical access control, supports hybrid search, and prevents data leakage.

    FigmaSoftware EngineerSystem DesignOnsite
  • Prepare for a real-time collaborative comments system design prompt for shared documents or canvases. The question covers durable comment writes, WebSocket fanout, reconnect backfill, ordering, permissions, hot-document scaling, and missed-update recovery.

    FigmaSoftware EngineerSystem DesignTechnical Screen
  • This problem tests the ability to design a distributed streaming architecture that aggregates real-time user activity while maintaining strict privacy and performance requirements.

    FigmaSoftware EngineerSystem DesignTechnical Screen
  • This question evaluates system design competence for building large-scale, near-real-time trending and feed systems, testing skills in streaming ingestion, event modeling, ranking algorithms, caching, consistency and fault-tolerance, capacity planning, and operational observability within the System Design domain.

    FigmaSoftware EngineerSystem DesignOnsite
  • This question evaluates a candidate's ability to design reliable, fault-tolerant asynchronous job scheduling systems, covering concepts such as immediate versus delayed execution, failure handling, retries, duplicate execution prevention, worker crashes, scheduler failover, and the feasibility of exactly-once semantics.

    FigmaSoftware EngineerSystem DesignOnsite
  • Design an automated, multimodal evaluation platform to benchmark and monitor a canvas-based design-editing agent across deterministic property updates and subjective creative directives.

    FigmaSoftware EngineerML System DesignOnsite
  • This question evaluates a candidate's competency in designing and operating a monolithic application on Kubernetes, covering platform engineering topics such as deployment topology, networking and traffic management, CI/CD and rollout strategies, resource management and autoscaling, stateful dependencies, observability, incident response, and disaster recovery. Commonly asked in System Design interviews to assess practical application of distributed systems and SRE principles—specifically the ability to reason about operational trade-offs, reliability, and incremental migration paths—this prompt sits in the System Design domain and emphasizes practical application with architectural reasoning rather than purely conceptual understanding.

    FigmaSoftware EngineerSystem DesignHR Screen
  • This question evaluates a candidate's understanding of reversible state mutation and undo/redo semantics, covering batching, inverse operation representation, operation compression, failure handling, and choice of data structures for edit history.

    FigmaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • A screening question for an ML engineer role focused on post-training for agents. It asks you to present an end-to-end project you owned, defend its technical decisions and strategy, explain how you designed and ran its evaluation, and say why you are leaving your current company and where your career is heading.

    FigmaMachine Learning EngineerBehavioral & LeadershipTechnical Screen
  • This question assesses your ability to navigate professional disagreement, exercise empathy towards cross-functional stakeholders, and leverage data to resolve conflicting viewpoints.

    FigmaData ScientistBehavioral & LeadershipOnsite
  • This question tests the ability to define a comprehensive measurement framework for a B2B SaaS collaborative tool, balancing growth, engagement, and retention metrics.

    FigmaData ScientistAnalytics & ExperimentationTechnical Screen
  • This question tests state management through a Command pattern implementation for undo/redo operations and spatial reasoning using depth-first search (DFS) to identify connected components in a 2D grid.

    FigmaMachine Learning EngineerCoding & AlgorithmsOnsite
  • This question tests your ability to articulate a complex technical project, specifically focusing on decision-making under ambiguity, architectural trade-offs, and your role in cross-functional collaboration.

    FigmaSoftware EngineerBehavioral & LeadershipTechnical Screen
  • This question evaluates quantitative compensation analysis, negotiation strategy, and stakeholder communication skills for a Data Scientist role, focusing on modeling offer components and articulating numerical trade-offs.

    FigmaData ScientistBehavioral & LeadershipHR Screen
  • This question assesses a candidate's ability to communicate technical complexity to a business audience, demonstrate ownership, and articulate the link between statistical results and product outcomes.

    FigmaData ScientistBehavioral & LeadershipOnsite
  • This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Share behavioral stories states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    FigmaSoftware EngineerBehavioral & LeadershipTechnical Screen
  • Determine if a sequence of structural tokens maintains valid scoping rules—never closing an un-opened level and closing all levels by the end. Tests single-pass balance checking, edge-case handling for empty streams, and O(1) space optimization.

    FigmaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates SQL proficiency—specifically joins, filtering, type casting, temporal aggregation, window functions or deduplication, and deterministic tie-breaking—by requiring derivation of first non-creator view times and per-creator collaborator counts from event tables.

    FigmaData EngineerCoding & AlgorithmsTechnical Screen

Figma interview experiences

All 4 →
  • Agent Post-training Phone Screens and a Colab PyTorch Pipeline I Could Not FinishFigma · Machine Learning Engineer · Technical Screen · Oct 2026

    Sharing my Figma MLE interview experience (Agent Post-training). A recruiter reached out to me in mid-September. The role is mainly about Agent-related post-training work, and I then scheduled a half-hour recruiter call. One: Recruiter screen (30 mins) The recruiter described the background of the role and what the work involves, then asked about my background and a few common BQs, including: Wal…

  • A Low-Energy Hiring Manager Round Worth AvoidingFigma · Data Scientist · Onsite · Jul 2026

    Sharing my interview experience with Figma DS for the HM round — overall it was really bad. This is the only company where I finished the interview and immediately wanted to warn everyone else away from it. They started by asking about my background and introducing the role. The HM seemed really low energy, like they were just going through the motions. They asked about a project and my work back…

  • Rejected 2 Days After a 5-Round Virtual OnsiteFigma · Software Engineer · Onsite · Jul 2026
    Rejected

    After the recruiter call, the first round was HM + coding. The VO (final onsite) was 5 rounds — coding + 2 system design rounds + behavioral + a project deep dive. I hadn't seen the AI-flavored system design round anywhere else before. The VO was spread across 3 days, and I got rejected 2 business days after that. The HM round asked about past projects, not much technical. The first coding round…

  • A System Design Interviewer Who Wouldn't Engage or ListenFigma · Software Engineer · Onsite · Sep 2025

    First off, my overall interview experience with Figma was really good — the HR/recruiter was responsive and helpful throughout the whole process, and after the onsite they gave me detailed feedback. Every interviewer I got, except for the one I'm about to go into detail about, was pretty nice. This interviewer had me for the system design round, and the question was the classic "trending articles…

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