Research Scientist - Decentralized Training and Inference Verification

About the job

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 https://arxiv.org/abs/2607.13332). 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 https://pluralis.ai/blog/a-third-path-protocol-learning/.

Our training and inference network is trustless, and the workers are GPUs scattered across the world. Many things can go wrong in this system. An inference worker can return tokens from a cheaper model, or a highly quantized version of the one it's supposed to run. Even with the right model, it can sample with the wrong parameters. Training faces its own attacks, and Agora showed what a permissionless run deals with in practice: participants who disrupt training by dropping updates or flooding the system, free-riders who submit trivial work and collect the rewards, poisoned updates that plant backdoors, attempts to extract private data from gradients and activations, and contributors who inflate their reported work to claim rewards they didn't earn. Sentinel https://arxiv.org/abs/2603.03592 is our first published answer on the training side. Your primary role is to come up with efficient algorithms and systems that verify the work: that tokens came from the claimed model and sampling parameters, and that training contributions are what they claim to be.