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Standard opener but I always fumble the pacing on these.
Structure your answer as a concise narrative that highlights 2-3 key projects, emphasizing your role, the technologies used, and the impact. Focus on how you navigated ambiguity and adapted to changing requirements, aligning with Microsoft's emphasis on adaptability. Keep it under 2 minutes, then invite follow-up questions.
Pro tip: Quantify your impact with metrics (e.g., latency reduction, user growth) and explicitly connect how you handled ambiguous situations to show you thrive in Microsoft's fast-paced environment.
Briefly introduce your overall experience and the themes you'll cover, such as backend development, cloud services, or cross-functional collaboration.
Select 2-3 projects that demonstrate technical depth, impact, and adaptability. For each, describe the context, your role, and the technologies used.
For each project, explain a specific challenge or ambiguity you faced and how you adapted your approach to overcome it.
Share measurable outcomes (e.g., performance improvements, cost savings, user adoption) to demonstrate the value of your work.
Tie your experiences back to the skills and qualities needed for the Software Engineer role at Microsoft, showing enthusiasm for the opportunity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the one I was least prepared for going in.
Start by giving a concise summary of your familiarity with LLMs, then explain the core mechanisms in simple terms, and finally connect it to practical software engineering implications. Emphasize your hands-on experience with LLM APIs and the trade-offs you've considered when integrating them.
Pro tip: Show that you understand the limitations and costs of LLMs, not just the hype. Mention specific examples of how you've mitigated issues like hallucinations or latency in your projects.
Briefly state your level of experience with LLMs, including any projects or courses, to set the context.
Describe the transformer architecture, training process, and inference in accessible terms, highlighting key concepts like attention and tokenization.
Talk about how LLMs are integrated into software, including API usage, prompt engineering, and handling responses.
Mention common challenges such as cost, latency, accuracy, and ethical considerations, and how you've addressed them.
Relate your understanding to the software engineer role at Microsoft, emphasizing scalability, reliability, and innovation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.