Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Pre-Training, pitched at the medium 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
Explain the three parallelism strategies: data parallel, pipeline parallel, and tensor parallel
Describe ZeRO optimizer stages and how they reduce memory per GPU in data-parallel training
Explain gradient accumulation, checkpointing, and why they are essential for large model training
Reason about communication bottlenecks (all-reduce, all-gather) in multi-node training runs
Topics covered
Data ParallelismCommunication PrimitivesGradient AccumulationMemory OptimizationPipeline ParallelismTensor ParallelismZeRO OptimizerMixed Precision3D ParallelismTraining StabilityCommunication BottlenecksFrameworks
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In plain terms, what is data parallelism and why is it the most common starting point for training large models?
“In mixed-precision training with FP16, why do we keep a master copy of weights in FP32 rather than just training entirely in FP16?
“You're planning to pre-train a 70 billion parameter model across 512 GPUs. How would you think about which parallelism strategies to combine and why?