How Scale AI Interviews in 2026: Process, Questions & What They Score

1. 01

   Implement an LRU-cache-style or task-scheduler data structure that manages priority and eviction/expiry constraints (e.g., 'Task Scheduler with Cooldown').

   Tests data-structure design: hash map plus linked list or heap for O(1) eviction with cooldown and priority rules.

2. 02

   Design and implement a rate limiter (e.g., a 'Logger Rate Limiter') or task queue that guarantees at-least-once delivery and handles worker/consumer failures gracefully.

   Tests limiter and queue design: token buckets, at-least-once delivery, and graceful worker-failure handling.

3. 03

   Given a stream of annotation/labeling tasks, process them with time-window aggregation (e.g., compute rolling metrics per worker per hour).

   Tests streaming aggregation: windowed per-worker rollups with efficient incremental updates.

4. 04

   Classic tree/graph problems: N-ary tree DFS, lowest common ancestor (LCA), binary tree level-order traversal (BFS), topological sort (course-schedule style).

   Tests core tree and graph patterns: DFS, lowest common ancestor, BFS level order, and topological sort.

5. 05

   Frequency-ordering problems (e.g., sort characters/items by frequency) and interval-merging problems (Merge Intervals, Meeting Rooms II style).

   Tests hash-map frequency counting and the sort-then-merge pattern for overlapping intervals.

6. 06

   Model a card game (a Texas Hold'Em-style poker hand evaluator is a specifically and repeatedly reported example, including a 'wildcard/joker' follow-up twist) or similar multi-rule, multi-state simulation with clean object-oriented design and edge-case handling. This is described as one of Scale's more distinctive, frequently-recurring questions.

   Tests object-oriented modeling of layered game rules; the poker evaluator rewards clean hand ranking and edge cases.

7. 07

   Write clean, testable code and reason aloud about complexity and edge cases rather than just producing a brute-force answer. Interviewers are reported to weight communication and code quality heavily.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Design a scalable data-labeling/annotation pipeline for multi-modal input (images, video, LiDAR, text), e.g., for an autonomous-driving customer.

   System design: they probe multi-modal ingestion, task queuing, human review stages, and throughput at scale.

2. 02

   Design a real-time feedback loop or active-learning system that routes model predictions back through human review.

   System design: tests routing model predictions through human review and feeding corrections back to training.

3. 03

   Design an LLM evaluation platform that can run enterprise prompt sets against multiple models and score/rank outputs for quality.

   System design: they probe running prompt sets across models, scoring pipelines, and output ranking.

4. 04

   Design a task-routing/queueing system that assigns annotation work to thousands of concurrent human workers with SLA-based priority, worker-skill matching, and quality/consensus controls.

   System design: tests queueing with SLA priority, worker-skill matching, and consensus-based quality control.

5. 05

   Design a workflow orchestrator for an RLHF pipeline (response generation, preference ranking, model training feedback, auditability).

   System design: they probe RLHF stage orchestration: generation, ranking, training feedback, and auditability.

6. 06

   Design multi-tenant data isolation/storage architecture for enterprise customers with large media (video, LiDAR, audio) volumes, covering tiered storage and CDN/prefetching strategies.

   System design: tests tenant isolation for large media plus tiered storage and CDN prefetch strategy.

7. 07

   Interviewers reportedly probe clarifying questions specifically about data format, expected task volume/concurrency, redundancy/consensus requirements (inter-annotator agreement, gold tasks), and single-step vs. multi-step vs. ML-assisted annotation workflows. Candidates are expected to raise these dimensions unprompted.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Walk through a computer-vision or NLP project you've built end to end; what were the accuracy/latency trade-offs?

   Tests ML depth on a real project; expect probing on the accuracy versus latency tradeoffs you made.

2. 02

   How would you detect and mitigate overfitting vs. underfitting, and which model types are most prone to each?

   Tests ML fundamentals: diagnosing overfitting versus underfitting and which model classes are prone to each.

3. 03

   Explain supervised vs. unsupervised learning and when you'd choose k-means clustering or linear regression (including implementing k-means and choosing k), plus known pitfalls.

   Tests ML basics: supervised versus unsupervised choices, implementing k-means, and picking k sensibly.

4. 04

   How would you design a next-word-prediction or product-recommendation system that also needs to support human review/QA?

   Tests ML system design that includes human review; they probe how QA requirements shape the architecture.

5. 05

   How would you evaluate the quality of a labeling workforce or a model against ambiguous ground truth?

   Tests measurement thinking: inter-annotator agreement, gold tasks, and evaluation against ambiguous ground truth.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   How would you think about pricing or pay structures for a data-labeling marketplace?

2. 02

   How would you prioritize a roadmap when 80% of the value comes from 20% of the effort (tie-in to the 'Find the 20%' credo)? Treat this as an illustrative prep angle rather than a word-for-word reported question.

1. 01

   Product rounds commonly include an estimation or market-sizing question in addition to a straight product-sense question; be ready to pivot between the two mid-interview.

2. 02

   Walk through how you'd partner with an engineering team to scope and ship a feature under time pressure.

1. 01

   Discuss a time you had to make an accuracy vs. throughput/cost trade-off in an ML system, or had to communicate a model-behavior/data-quality tradeoff to a non-technical client stakeholder.

1. 01

   Tell me about yourself and why you want to work at Scale AI specifically.

2. 02

   Tell me about a time you shipped something under an aggressive or 'impossible' deadline. What did you deprioritize or cut, and why?

3. 03

   Describe a time you owned a system or outcome end-to-end, not just your individual piece of it.

4. 04

   Tell me about a time you disagreed with a manager or teammate. How did you resolve it?

5. 05

   What is the most impressive/hardest thing you've done, professionally or otherwise?

6. 06

   What would your colleagues say are your strengths and weaknesses?

7. 07

   Describe a time you had to make a decision quickly with incomplete information.

8. 08

   How do you feel about a fast-paced, high-intensity work environment with long hours. Give an example of when you've operated that way before.

1. 01

   Give an example of working directly with a customer to translate a vague need into a concrete product requirement.