Amazon·Software Engineer·Onsite - Behavioral / Leadership
Jun 2026
A long behavioral round at Amazon for a software engineer role, roughly 55 minutes, split between digging deep into a past project and talking through how I actually use generative AI day to day. The interviewer wasn't just collecting answers, they kept interrupting to push further on anything that sounded interesting, which was both good and kind of exhausting.
- Walk me through your proudest project from start to finish: what motivated it, what you personally contributed, the technical decisions you made, any trade-offs, failures along the way, and what the measurable impact was.
- How do you actually use generative AI tools like LLMs or coding assistants in your day-to-day work? What workflows have you built around them, where do they fall short, and how do you verify what they produce?
- Can you explain the difference between training and inference for large language models, and how that distinction affects how you think about using them in production?
- How does the attention mechanism work in transformer-based models, and why does it matter for how these models handle context?
- What is tokenization in the context of LLMs and what are the practical implications of how a model tokenizes input?
“This took up a big chunk of the interview.” The rest of the author's notes on Software Engineer interview at Amazon, Onsite - Behavioral / Leadership round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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