Scale Ai Interviews

Round 1 — Behavioral Questions + Backend Practical

This round began with standard behavioral questions where I discussed my previous projects and experiences. The main part was a backend practical, conducted using Python. The task was centered around building a lightweight load balancer. I had to address several key implementation points:

• Worker state management, considering states like active, overloaded, or unreachable.
• Designing a task queue along with a priority scheduling mechanism.
• Thinking about how to support scalability, for instance, by allowing dynamic joining of worker nodes.

The design required me to think about task-dispatch logic, a worker heartbeat mechanism, and a failover strategy. The expectation was to produce code within a limited timeframe, and the bar for this round felt quite high.

Round 2 — Coding: Clock-hand angle

The second round was a live coding session on CoderPad. The problem itself wasn't overly difficult, but precision was crucial. I was asked to compute the angle between the hour hand and the minute hand, given a time string like “3:45”. Beyond just getting the correct answer, I had to clearly explain my approach, especially the derivation of each part of the angle calculation. The core idea was to recognize that the minute hand moves 6° per minute, and the hour hand moves 30° per hour plus 0.5° per minute. I calculated each hand's angle relative to 12 o'clock, took the absolute difference, and then adjusted it to be the smaller angle (subtracting from 360° if greater than 180°). A follow-up question explored how to adapt the formula if the input included seconds or even milliseconds.

Round 3 — System Design

This was a system design round, framed as a training session. I was tasked with designing a system similar to Ticketmaster. Rather than getting bogged down in back-of-the-envelope calculations or complex distributed systems right away, I focused on clearly mapping the user flow and drawing a diagram of all the necessary components. Key requirements I had to address included:

• Strategies for handling flash-sale scenarios with a high volume of concurrent users.
• Implementing a timeout mechanism on the purchase page and defining actions if payment isn't completed within that window.
• Managing the situation when tickets become sold out.
• Ensuring that users who successfully complete payment are guaranteed to receive their tickets.
• Designing a waitlist system to notify users next in line when tickets become available due to returns.

Round 4 — Behavioral Questions + Coding

The final round combined behavioral questions with another coding challenge. The behavioral part included standard questions:

• "Tell me about a time you had to learn something quickly." Here, I emphasized a structured learning approach, mentioning resources like API documentation, codebase walkthroughs, shadowing colleagues, and building proof-of-concept projects.
• "Tell me about a time you disagreed with a teammate or manager." I focused on supporting my stance with data-driven or customer-impact reasons, demonstrating my ability to accept valid feedback and work towards a win-win outcome.
• "Tell me about a challenging project you worked on." I used the STAR structure to narrate a story with a significant business impact, quantifying my contributions (e.g., "improved performance by 40%," "reduced latency to P99 < 200ms").

The coding problem in this round was to find the Lowest Common Ancestor (LCA) of two nodes in a tree, given that I only knew each node’s list of children. I discussed several approaches. One involved using DFS to compute each node’s parent and depth, then raising the deeper node until depths matched, and finally moving both nodes up simultaneously until they met. Another approach I considered was a DFS from the root where each node would return a pair of booleans indicating whether it could reach the two target nodes; the first node returning (true, true) would be the LCA. I made sure to explain my ideas, clarifications, comments, and test cases in detail for these approaches.

During my interview at Scale AI, I encountered a significant system design challenge. The problem focused on creating a robust and efficient system capable of managing tasks and jobs, and then processing these tasks through a third-party LLM service. This required careful consideration of data fetching from MongoDB, job creation for up to 5000 tasks, and synchronous interaction with the LLM service, handling batching of 10 tasks per API request.