Scale AI interview questions

Showing 1–29 of 29 results

• Answer common project leadership questionsEasy

  This question evaluates ownership, self-awareness, communication, prioritization, and growth mindset through prompts about proud projects, responses to feedback, time-pressured work, side projects, and personal strengths and weaknesses.

  Scale AISoftware EngineerBehavioral & LeadershipOnsite
• Build a CSV-to-JSON Classification ServiceEasy

  Implement a resilient data pipeline service that parses CSV records, enriches them using an external classification client with validation and retries, and writes ordered, structured JSON Lines output safely.

  Scale AIBackend EngineerSoftware Engineering FundamentalsOnsite
• Compute clock hand angle with seconds and millisecondsMedium

  This question evaluates understanding of angular geometry and numeric precision in time-based systems, testing competency in parsing varying time formats and computing continuous clock-hand positions; it is in the Coding & Algorithms domain and requires both conceptual understanding of angular motion and practical application in precise computation. Interviewers commonly ask this problem to assess an applicant's ability to model continuous motion, handle 12/24-hour representations and edge cases, and manage seconds- and milliseconds-level precision when deriving and implementing numerical formulas.

  Scale AISoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute party time blocks per neighborhoodMedium

  This question evaluates a candidate's ability to perform interval merging and temporal aggregation across joined datasets, testing algorithmic efficiency, data-processing skills, and handling of sorting and grouping in the Coding & Algorithms domain.

  Scale AISoftware EngineerCoding & AlgorithmsOnsite
• Connect Your Hardest Project to Your Interest in a RoleEasy

  Tests a candidate's ability to deliver a crisp, unified narrative connecting their hardest technical project, personal ownership, and key learnings directly to their interest in Scale AI.

  Scale AISoftware EngineerBehavioral & LeadershipTechnical Screen
• Debug ML pipeline and build text parserMedium

  This question evaluates practical machine learning engineering skills, including debugging unfamiliar codebases, parsing noisy text data, and identifying subtle defects such as train/test leakage or shape mismatches. It tests applied Python proficiency and systematic reasoning under time pressure, core competencies assessed in ML engineer technical interviews.

  Scale AIMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen
• Debug a Project Assignment CodebaseMedium

  Tests your ability to manage state, enforce filtering rules, and maintain clean public API contracts in an asynchronous or stateful task assignment system.

  Scale AISoftware EngineerSoftware Engineering FundamentalsOnsite
• Describe how you learn quickly in new domainsMedium

  This question evaluates a candidate's rapid-learning and adaptability skills, emphasizing prioritization, structured learning processes, and the ability to balance knowledge acquisition with delivery.

  Scale AISoftware EngineerBehavioral & LeadershipTechnical Screen
• Describe how you resolve conflicts at workMedium

  This question evaluates a candidate's conflict-resolution, communication, and interpersonal leadership competencies in cross-functional team and stakeholder interactions.

  Scale AISoftware EngineerBehavioral & LeadershipTechnical Screen
• Design CSV upload endpoint with GPT classificationMedium

  Implement a backend processing pipeline that parses an uploaded CSV file, enriches each text record via an external classification client, and persists structured results to a local JSON store.

  Scale AISoftware EngineerSoftware Engineering FundamentalsOnsite
• Design a Durable Task Scheduling ServiceEasy

  Design a resilient, distributed task scheduling service that decouples scheduling from execution, supporting both delayed one-time tasks and recurring cron jobs with strong durability and at-least-once delivery.

  Scale AIBackend EngineerSystem DesignOnsite
• Design a Streaming Job SchedulerEasy

  This question evaluates task scheduling, deadline-based prioritization, dependency management, and distributed coordination competencies, encompassing algorithmic/data-structure reasoning as well as correctness under concurrency and failure conditions.

  Scale AISoftware EngineerSystem DesignTechnical Screen
• Design a large-scale ticketing systemMedium

  This question evaluates skills in large-scale system design, including distributed systems, concurrency control, transactional correctness, data modeling, API design, and integration with external payment providers.

  Scale AISoftware EngineerSystem DesignTechnical Screen
• Design an LLM API pipelineEasy

  This question evaluates competency in integrating hosted LLMs into user-facing applications, including API request design, prompt engineering for structured output, output validation, latency and failure handling, and production readiness.

  Scale AISoftware EngineerML System DesignOnsite
• Design and Simulate a Four-Player Card GameMedium

  Work through a three-stage object-oriented card game covering dealing, follow-suit trick state, configurable scoring, and deterministic tests.

  Scale AISoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design pipeline using classification and embedding servicesMedium

  This question evaluates a candidate's proficiency in ML system design, covering API design, service orchestration, data storage modeling, scalability strategies, and reliability/observability when integrating black-box classification and embedding services.

  Scale AISoftware EngineerML System DesignOnsite
• Explain LLM post-training methods and tradeoffsEasy

  This question assesses the candidate's understanding of the LLM post-training pipeline, specifically how to transition a foundation model into a helpful, safe assistant through various alignment techniques.

  Scale AIMachine Learning EngineerMachine LearningOnsite
• Explain Transformers, attention, decoding, RL, and evaluationHard

  This question evaluates understanding of transformer architectures, self- and cross-attention mechanisms, autoregressive decoding and sampling strategies, reinforcement learning–based fine-tuning (RLHF), evaluation methodologies, and training/inference optimization techniques.

  Scale AIMachine Learning EngineerMachine LearningTechnical Screen
• Find LCA in a N-ary tree via DFSMedium

  This question evaluates understanding of tree algorithms and recursive DFS traversal for computing the lowest common ancestor in an N-ary tree, including handling node references, subtree detection, and correctness in a single traversal.

  Scale AISoftware EngineerCoding & AlgorithmsTechnical Screen
• Handle customer engagement and manager-rating questionsEasy

  This question evaluates stakeholder management, leadership principles, conflict-resolution skills, and self-awareness, emphasizing interpersonal communication, influence, and accountability.

  Scale AIMachine Learning EngineerBehavioral & LeadershipOnsite
• Implement Dependency-Aware Task SchedulerHard

  Design and implement a dependency-aware task scheduler that supports dynamic task registration, cycle detection, priority-based ready task retrieval, and state transitions.

  Scale AISoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement Hook and Modify UI InteractionsMedium

  This question evaluates a candidate's ability to implement reusable React hooks, manage asynchronous data fetching with loading/error/refetch semantics, transform API responses, and wire component state for modal, collapsible list, and button-driven UI interactions.

  Scale AISoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Implement a Dependency-Aware Task SchedulerEasy

  This question evaluates skills in dependency management, deadline-driven scheduling and algorithmic data-structure design, focusing on handling directed acyclic graphs and ordering constraints.

  Scale AISoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement a Task ProcessorMedium

  This problem tests your ability to manage tasks with dependencies using a combination of graph-based topological sorting (Kahn's algorithm) and a priority queue to ensure deadlines are respected.

  Scale AISoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement multi-head attention and LLM samplingEasy

  This problem tests your ability to implement the core mechanics of a Transformer's attention mechanism and the logic required to sample the next token from a model's output logits.

  Scale AIMachine Learning EngineerCoding & AlgorithmsOnsite
• Implement universal adversarial attack on GPT-2Medium

  Machine learning robustness prompt on safely evaluating universal adversarial prompts for GPT-2, covering threat modeling, differentiable optimization concepts, transferability, metrics, safety controls, and mitigation strategies without deployment-ready jailbreak details.

  Scale AIMachine Learning EngineerMachine LearningTechnical Screen
• Present a Metrics-Driven Technical ProjectEasy

  Tests your ability to present a past technical initiative clearly to peers, articulating technical architecture, quantifiable metrics, personal ownership, and real-world tradeoffs.

  Scale AISoftware EngineerBehavioral & LeadershipOnsite
• Quantify impact of your projects using STARMedium

  This question evaluates a candidate's ability to use the STAR method to articulate project ownership, quantify technical and business impact, and demonstrate leadership, communication, and metrics literacy for a Software Engineer role.

  Scale AISoftware EngineerBehavioral & LeadershipTechnical Screen
• Schedule Ready Tasks by DeadlineMedium

  This question evaluates understanding of task scheduling and dependency management in directed acyclic graphs, along with algorithmic selection and data-structure-based optimization for repeated minimum-deadline retrieval.

  Scale AISoftware EngineerCoding & AlgorithmsTechnical Screen