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About this interview
A technical interview on Transformer Architecture, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Compare MHA, MQA, GQA, and MLA (DeepSeek) in terms of memory-throughput tradeoffs
Explain Mixture-of-Experts: sparse routing, load balancing, active vs total parameter count
Describe tokenization approaches (BPE, SentencePiece) and the role of vocabulary size
Articulate scaling laws and the Chinchilla compute-optimal finding, including emergent abilities
Topics covered
KV Cache FundamentalsKV Cache ScalingBPE TokenizationTokenization ApproachesVocabulary Size TradeoffsScaling LawsChinchilla Compute-OptimalityCompute-Optimal TrainingEmergent AbilitiesMHA vs MQAMQA TradeoffsGQA DesignGQA in PracticeMLA / DeepSeek Attention
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In multi-head attention, what is stored in the KV cache and why does it exist at all?
“Can you describe the Multi-head Latent Attention approach used in DeepSeek? What is the low-rank compression idea and what does it achieve?
“How do scaling laws for pre-training loss translate — or fail to translate — into downstream task performance? What complicates this connection?