Interviewing for ML/AI Engineers (www.moderndescartes.com)

In a recent essay detailing their experience interviewing for ML/AI engineering positions, a frustrated candidate argues against the traditional ML system design interview format, highlighting its inadequacies and proposing improvements. The author reveals that common pitfalls include vague scenarios, outdated questions, and overly simplistic evaluations that fail to adequately assess a candidate's real-world problem-solving skills. Such interviews often reinforce bad practices, like focusing excessively on theoretical knowledge rather than hands-on abilities, leading to poor hiring outcomes and suggesting a stagnation in the interviewing companies' technical knowledge. This candid critique is significant for the AI/ML community as it underscores the need for a reform in the hiring process, advocating for structured interviews that better reflect the complexities of real-world ML engineering work. The proposed framework emphasizes scenario-based questions that provoke critical thinking and practical application, alongside assessments of coding, data modeling, and math proficiency tailored to today's technology landscape. By revamping interview strategies, companies can better identify candidates capable of advancing in a fast-evolving field where understanding the nuances of modern ML is crucial for success.