The Scale AI The interview process covers a Take-home assignment, Tech Screen, BQ, ML basics assessment, and Coding Test, rigorously evaluating candidates' technical, communication, and project skills. Given the growing interest among students, this article breaks down each stage to help you prepare effectively. Given the growing interest among students, this article breaks down each stage to help you prepare effectively.
Take-home Assignment
Submit a data preprocessing or related task to showcase data handling and logical implementation skills. Include clear documentation and high-quality code. Keep code tidy, verify functionality with unit tests, and add detailed comments.
Technical Screening (Tech Screen - 1 hour)
Discuss Take-home Assignment solutions and improvements, and answer technical questions to test logical thinking and problem-solving. Review assignment key points and prepare optimization plans in advance. Assignment key points and prepare optimization plans in advance.
Back-to-back Interview (Total 2.5 hours)
- BQ (0.5 hour). Answer questions on past projects, conflict resolution, and career plans. Use the STAR method with real-life examples.
- ML (1 hour). Demonstrate knowledge of machine learning basics like model selection and data preprocessing. share practical model optimization cases and review key concepts.
- Coding Test (1 hour). Solve medium-difficulty algorithms, focusing on time complexity and efficient code. Familiarize with common data structures and keep code clear.
Additional HM Interview (0.5 hour)
Have in-depth conversations with the Hiring Manager about projects and backgrounds, with a detailed discussion on a key project, potentially running Have in-depth conversations with the Hiring Manager about projects and backgrounds, with a detailed discussion on a key project, potentially running.
Sharing of Key Question Types
- System Design
Build a black-box system around a Large Language Model (LLM). After users input requests, the system needs to receive these inputs asynchronously and split them into hundreds of segments. Each segment will call the LLM black-box service synchronously. After processing, the results must be fed back to users via notification. - Backend Practical
Step 1: CSV Reading and Dumping
Read two CSV files (Tasks.csv and Users.csv) and convert the content into a structured JSON file.
Step 2: LLM Classification Task
Use the provided LLM API to classify a specific column in the CSV. Write the classification results back to the JSON file. - Debug Practical
Examine your debugging and logical analysis abilities.
A table contains fields such as contributors, tasks, courses, etc. Implement logic to.
Assign tasks to eligible contributors according to priority; Each contributor must complete the specified courses (course prerequisite). The code consists of 5-6 files, with some functions marked "error-free" (unchangeable). The code consists of 5-6 files, with some functions marked "error-free" (unchangeable). Three test cases are provided; you must find and fix errors via debugging.
Final Reminder
Scale AI's interviews are fast-paced and technically demanding. Thorough preparation is key to performing well. We hope this guide helps you focus your efforts effectively.
For support during your preparation -be it facing challenges, lacking ideas, or short on time-ProgramHelp We'll guide you from application to job offer, ensuring you're fully supported every step of the way. We'll guide you from application to job offer, ensuring you're fully supported every step of the way.
