Design a Distributed Training Platform

It sounds like an ML question, but almost none of it is. The model is a given: a transformer with tens or hundreds of billions of parameters, and the training math is somebody else's job. You design the platform around it. Feed it, split it across chips that cannot each hold it, keep it alive while hardware fails underneath it, and save its progress so weeks of compute do not evaporate when one node dies.

Start from the constraint that forces every other decision: the model does not fit on one accelerator. Not the weights, not the optimizer states, not the activations. A single high-end training chip carries on the order of 80 to 96GB of high-bandwidth memory. The parameters plus the optimizer's master copy and moment estimates for a frontier model run to terabytes. So the model is cut into pieces that live on different chips. Those pieces trade numbers every step, and the network between them decides whether the fleet is computing or idling. That tension runs through the whole round.

The Anthropic angle

What is public about Claude's training is the shape of the fleet, not a line of its software. The internal stack, the scheduler, the checkpoint format, the exact parallelism layout stay a black box, so build this from general practice and say so. Anthropic has announced large multi-accelerator buildouts, with AWS on Trainium hardware and with Google on TPU pods. So the platform you describe has to abstract over more than one accelerator and more than one interconnect. That is not the single-vendor mental model most candidates walk in with. Everything else here, the 3D parallelism, the collective-communication math, the checkpoint cadence, comes from the open Megatron and DeepSpeed lineage and from how TPU and Trainium pods are known to work. Present it as how frontier training is built across the industry. An interviewer who works on the real thing will respect "this is the general design" and will catch a claim of inside knowledge you do not have.