Strategizing Your Preparation for Machine Learning Interviews

In this article, the author shares their personal experience of facing multiple rejections during job interviews early in their career, and how they eventually landed their dream job as a Machine Learning (ML) Engineer at Meta. The key to their success was not talent or luck, but consistent learning and targeted preparation. The author emphasizes the importance of understanding the broad spectrum of ML roles, which can significantly refine one's interview strategy, increase confidence, and help minimize uncertainties. ML roles can vary widely based on their primary technical responsibilities and area of specialization. The author provides a sample of job titles across the ML role spectrum, highlighting that each company defines these titles differently, and reviewing job descriptions is crucial. Understanding job requirements is crucial for two main reasons: it helps eliminate roles that do not fit one's goals, and it provides clues about the specific domain the job relates to. The author shares examples of how they identify keywords in job descriptions to map roles to the ML spectrum. The article then covers the most common interview rounds in ML, including ML Fundamentals/Breadth, ML Case Study/Depth, ML System Design, and ML Coding. The author provides a strategy for developing a preparation plan tailored to specific roles, starting with the fundamentals and then identifying the strategy based on the role's focus. For data/modeling roles, the author emphasizes the importance of understanding company/job-specific fundamentals, as well as preparing for domain-specific knowledge. For ML services and infrastructure roles, the focus shifts towards understanding the company/job-specific tech stack and domain-specific tradeoffs. The author also recommends researching company blogs and papers to gain insights into the team's or domain's challenges and potential interview questions, which can spark valuable conversations with the interviewer. In conclusion, the author emphasizes the importance of tailored preparation for ML interviews, as it helps address specific role and company demands, understand domain-specific nuances, and increase the chances of success. The author also suggests tracking one's progress and learnings throughout the preparation journey, as ML research advances rapidly, and new breakthroughs can change interview questions.