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 grabs.
Key ResponsibilitiesSolve 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.
Get the methods into runs: Work with the engineering team so your results land in live training runs, not just papers.
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.
Experience with foundation model pre-training, post-training, or RL.
Experience at proprietary, open-weight and open-source AI labs
Equity-Heavy Package: We offer significant ownership for key technical contributors in addition to a high base salary.
Remote-First Culture: Flexible work environment with team members distributed globally.
Visa Sponsorship: Optional full visa sponsorship and relocation support to either Australia or the US.
Open Problems: Training and serving frontier models on hardware you don't control, over networks you don't own, mostly has no published answers yet. You'll write some of the first ones.
We work remotely across the world, with the main teams in Australia and North America. You'll need to be comfortable working across timezones.
Applicants must have professional-level English proficiency (written and spoken).
Recruiters: we aren't looking for agency support at this time. We'll reach out if we need help.
We are backed by Union Square Ventures and other tier-1 investors, and we are a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We view AI and the world on a better path for everyone if we are able to implement the protocol for intelligence.
Skills Required
- PhD in machine learning
- Publications in top-tier conferences such as NeurIPS, ICML, or ICLR
- Hands-on experience with large-scale distributed training
- Experience with compression strategies
- Strong programming ability in PyTorch
- Professional-level written and spoken English proficiency
- Experience with foundation-model pre-training, post-training, or reinforcement learning
- Experience at proprietary, open-weight, or open-source AI labs
What We Do
Pluralis is developing a protocol that facilitates collaborative training and ownership of foundation models.
