Description
Define the joint optimization of model compression and silicon architecture for Amazon's next generation of edge and cloud inference accelerators. Your work will set the technical targets that propagate across the model, compiler, runtime, and silicon stack.

We are hiring a Principal Applied Scientist to be the technical leader who closes the loop between compression science and silicon design. Today's generation ships advanced quantization and large-model distillation in production, running multi-billion parameter language models at inference economics typical of much larger systems. Future generations target significantly larger models at the edge and in the cloud. You will be a principal architect of the next-generation accelerator and of the compression algorithms it executes natively. Few roles in the industry let one technical leader influence the model, the compiler, the runtime, and the silicon without organizational friction. This is one of them.
<...