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About this interview
A technical interview on ML Infrastructure: Training Systems and Data Pipelines, 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
Explain the role of a computational graph and how automatic differentiation is implemented in frameworks like PyTorch
Compare data parallelism, model parallelism, and pipeline parallelism for distributed training and their communication costs
Describe the role of a parameter server and all-reduce collectives in gradient synchronization
Explain how hardware accelerators (GPUs, TPUs) are scheduled and why memory bandwidth is often the binding constraint
Topics covered
Computational Graph BasicsAutomatic DifferentiationStatic vs Dynamic GraphsGraph OptimizationDistributed Training MotivationData ParallelismModel ParallelismHybrid ParallelismCollective CommunicationsParameter Servers vs AllReduceAsynchronous TrainingParameter ServersMemory OptimizationHardware Accelerators
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“When you talk about a computational graph in the context of machine learning, what are the two fundamental building blocks, and how do they relate to each other?
“Synchronous training stalls when a slow node, called a straggler, holds up the AllReduce barrier. How does a parameter server with asynchronous training address this, and what does it sacrifice?
“Mixed-precision training with BF16 or FP16 reduces memory and speeds up matrix operations, but naive implementation causes gradient underflow. How does the mixed-precision recipe address this, and why do practitioners now often prefer BF16 over FP16?