← Microsoft Interview Insights
You never really know which projects they'll latch onto.
Start with a high-level summary of your ML experience, then select 2-3 projects that best demonstrate depth and relevance to the role. For each, be prepared to discuss the problem, approach, trade-offs, and impact, and invite follow-up questions to show confidence and depth.
Pro tip: Proactively mention a trade-off or limitation you encountered and how you addressed it—this shows maturity and self-awareness, and often steers the conversation toward your strengths.
Give a brief overview of your ML background, highlighting the breadth and depth of your experience. Mention the types of problems you've solved and the technologies you've used.
Choose 2-3 projects that are most relevant to the role and company, and that you can discuss in depth. Prioritize projects with clear impact and technical complexity.
For each project, describe the problem, your approach, key technical decisions, trade-offs, and the outcome. Use metrics to quantify impact where possible.
Explicitly discuss trade-offs you made (e.g., model complexity vs. latency, accuracy vs. interpretability) and what you learned. This demonstrates critical thinking.
After presenting each project, invite the interviewer to ask follow-up questions. This shows confidence and allows you to showcase depth on topics you know well.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.