Build a tiny autograd engine from scratch: a scalar Value with backprop over a computation graph.
A build-it-yourself check on how PyTorch actually works under the hood. What matters is assembling a computation graph during the forward pass, local derivatives per op, and a topological-order backward pass that accumulates gradients. Below is a minimal engine.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A build-it-yourself check on how PyTorch actually works under the hood. What matters is assembling a computation graph during the forward pass, local derivatives per op, and a topological-order backward pass that accumulates gradients. Below is a minimal engine.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
