Grand Challenge is Amazon's internal incubator, similar to Google X — in fact it was founded in 2014 by Babak Parviz, the former Director of Google X. Right now it's mainly focused on applying AI to healthcare, and the project is highly confidential outside the team. That said, the interviewers and the hiring manager were warm and friendly, and the overall experience was positive.
The interview had three rounds in total:
Coding: They first asked about the details of my previous LLM project, then moved into LLM fundamentals — things like Transformer and MoE architecture, collective communication, and parallelism. After that I did one algorithm problem (the exact LeetCode problem name in my notes came out garbled, but it was solved with a max-heap approach).
LP: Project details, plus leadership-principle questions.
System design: Design an LLM quality validation system. I answered from three angles:
- Validate at different levels — layer-by-layer, tensor, logits, tokens.
- Validate with different methods — LLM-as-judge, loss function, human annotation.
- End-to-end system: the user issues an async command, it launches an EC2 instance, spins up a Triton server and vLLM, runs inference, collects the LLM's responses and uploads them to S3, and then runs them through the validation methods above.
If people are interested, I could write a separate post specifically about the LLM quality validation system, since it's something I actually worked on as part of my team and have some hands-on experience with.
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