Pluralis Research is pioneering Protocol Learning—a fully decentralised way to train and deploy AI models that opens this layer to individuals rather than well resourced corporates. By pooling compute from many participants, incentivising their efforts, and preventing any single party from controlling a model’s full weights, we’re creating a genuinely open, collaborative path to frontier-scale AI. If you want your work to shape the future of truly open innovation in artificial intelligence, join us.
Contribute to groundbreaking research in Protocol Learning during your PhD. Join Pluralis for a 6-month research internship focused on publishing.
This fixed-term position offering an opportunity to author foundational papers in an emerging field with access to significant compute and focused mentorship from senior scientists.
Publication-Oriented Research: Conduct novel research within the domain of Protocol Learning, with the explicit goal of publishing in tier-1 ML conferences (NeurIPS, ICML, ICLR).
Publication Track Record: Current PhD candidate with at least one publication in top-tier ML venues (NeurIPS, ICML, ICLR, etc.).
Research Focus: Are working in a core technical area relevant to frontier models.
Technical Depth: Strong theoretical understanding of deep learning and distributed systems principles.
Implementation Skills: Proficiency in PyTorch and experience with large-scale training infrastructure.
We only hire in Australia and the United States. Visa sponsorship is limited to these countries.
Applicants must have professional-level English proficiency (written and spoken).
Pluralis is a remote team across Australia and the US. You’ll need to be comfortable working across timezones and collaborating with a diverse, distributed group.
Recruiters: we aren’t looking for agency support at this time. We’ll reach out if we need help.
Backed by Union Square Ventures and other tier-1 investors, we’re a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We view the world as a better place if we are able to implement what we are attempting, and Protocol Learning as the only plausible approach to preventing a handful of massive corporations monopolising model development, access and release, and achieving massive economic capture. If this resonates, please apply.
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