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The Interview Process
Math & ML Theory Screen
Rigorous questioning on linear algebra, probability, and backpropagation mechanics. You cannot just call high-level PyTorch APIs; you must understand the math underneath.
Research Presentation
You present a published paper or a significant ML project. Expected to defend your methodology against world-class researchers (especially for Naver Clova roles).
Engineering/Deployment Panel
Naver expects its researchers to write production code. You will be tested on deploying models at scale (e.g., serving a billion-parameter LLM efficiently).
Real Naver Interview Questions
Practice these exact questions faced by previous AI Researcher / ML Engineer (Clova) candidates.
1We are training a massively multilingual Large Language Model (like HyperClova X) on hundreds of billions of tokens. Walk me through how you implement 3D parallelism (Data, Tensor, and Pipeline parallelism) using PyTorch Distributed to train across a cluster of 1,024 GPUs. (Distributed ML / Scale)
2You are building a personalized recommendation engine for Naver Webtoon. Collaborative Filtering is suffering from the 'cold start' problem for newly uploaded comics. How do you integrate content-based features (e.g., image analysis of the comic art) to improve early recommendations? (Applied ML / Recommendation Systems)
3Exactly derive the mathematics of the Attention Mechanism in a standard Transformer architecture. Why do we scale the dot products by the square root of the key dimension (d_k)? (ML Theory / Deep Math)
4(Value: Innovation) Tell me about a time you implemented a state-of-the-art paper from ArXiv, but it completely failed on your real-world, noisy dataset. How did you adapt the architecture to make it work in production? (Research Application)
5How do you evaluate generative AI models for safety and bias before deploying them to a Korean domestic audience, considering the specific cultural nuances and legal regulations regarding hate speech and defamation? (AI Ethics / Domain Knowledge)
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