Research Engineer - Geo-Distributed Inference
Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. Despite being dismissed as infeasible, we have made significant advances on this problem, most recently Agora, a permissionless run that pretrained an 8B model from scratch on consumer GPUs spread over the internet, with no single participant ever holding the full weights (tech report). While many of the core research problems have been solved, Protocol Learning unlocks a series of new challenges. For the mission in full, read A Third Path: Protocol Learning.
Our inference pipeline generates the rollouts for reinforcement learning (RL) training today, and it'll serve our models once they're trained. It also runs in a permissionless, trustless setting, which makes the usual serving problem much harder. The hardware is Macs and consumer GPUs owned by strangers, the network is the public internet, nodes join and leave mid-run, and the weights change under the server as training moves. Your primary role is to build the systems that keep this pipeline fast and reliable under these conditions.
