Pluralis Research works on Protocol Learning — decentralized, communication-efficient model-parallel training for foundation models. Backed by USV.
About the role
Solve the open problems : Identify the questions that block Protocol Learning at scale. communication efficiency, convergence under churn and staleness, heterogeneity, robustness to malicious participants. Publish in Tier-1 venues : The problems are hard and largely unclaimed. Solve them, and publish.
What they're looking for
- Research excellence (required) : PhD in machine learning with publications in top-tier conferences (NeurIPS, ICML, ICLR)
- Distributed ML experience : Hands-on experience in large-scale distributed training and compression strategies
- Implementation skills : Strong programming ability in PyTorch
- Mission alignment : You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI
More about this role
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 .
This setting breaks nearly every assumption of datacenter training and inference: communication-efficient training across different parallelism axes, fault tolerance as nodes join and drop mid-run, heterogeneous compute and networks, and robustness to malicious participants. Our published methods include Subspace Networks , Factored Gossip DiLoCo , AsyncMesh , and Sentinel .
As a Research Scientist you work on the problems that stay open as we push from the 8B run toward frontier scale, and you publish what you find. These are foundational papers up for...
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