Running machine learning (ML) algorithms at our scale often requires solving novel systems problems. As a Performance Engineer, you'll be responsible for identifying these problems, and then developing systems that optimize the throughput and robustness of our largest distributed systems. Strong candidates here will have a track record of solving large-scale systems problems and will be excited to grow to become an expert in ML also.

Implement low-latency high-throughput sampling for large language models

Implement GPU kernels to adapt our models to low-precision inference

Write a custom load-balancing algorithm to optimize serving efficiency

Build quantitative models of system performance

Design and implement a fault-tolerant distributed system running with a complex network topology

Debug kernel-level network latency spikes in a containerized environment