Coding Interviews for ML Roles

ML coding preparation benefits from practicing both software fundamentals and small machine-learning implementations. Microsoft Careers’ public interview guide says candidates may need to clarify a problem, plan, code in a language they know, test edge cases, and explain algorithm choices and complexity. Anthropic’s public technical-interview page says its programming-focused interviews use shared coding environments and emphasize reasoning, tradeoffs, writing, running, and debugging solutions; this describes Anthropic’s process, not other companies’ hiring loops. Machine-learning practice can include implementing a metric, a gradient update, a data transformation, or a simple clustering step. Google’s Machine Learning Crash Course includes programming exercises for linear regression, gradient descent, and classification. Such exercises are learning materials, not evidence that a particular employer will ask for a from-scratch implementation. In a coding discussion, correctness, readable code, input assumptions, numerical behavior, and tests matter alongside an algorithm’s asymptotic cost. Prepare by writing code in a familiar language, talking through assumptions, and checking simple and boundary cases. For ML tasks, preserve label and feature alignment, handle shapes and missing values deliberately, and explain how you would test the function on a small hand-worked input. For standard algorithm questions, choose a suitable data structure and discuss time and space complexity. Actual interview formats depend on the employer and role; review the current interview instructions and job requirements rather than assuming one shared question bank.