Research Scientist
: Job Details :

Pluralis Research

Pluralis Research is pioneering Protocol Learninga fully decentralized way to train and deploy AI models that opens this layer to individuals rather than well-resourced corporates. By pooling compute from many participants, incentivizing 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.

Key Responsibilities

Publish in Tier-1 Venues: At the core of Protocol Learning are hard research problems. There are foundational papers up for grabs. Solve these problems, and publish.

What We're Looking For

Research Excellence: PhD in Machine Learning, Maths or Physics with publications in top-tier conferences (NeurIPS, ICML, ICLR).

Distributed ML Experience: Exposure to distributed training, or federated learning.

Implementation Skills: Strong programming abilities in PyTorch, with experience training models across multiple devices.

Bonus: Experience with large language models, post training, RL etc.

Compensation & Benefits

Equity-Heavy Package: We offer meaningful ownership or token allocations for key technical contributors.

Competitive Base: Pluralis is hiring the best.

Visa Sponsorship: Optional full visa sponsorship and relocation support to either US or Australia.

Remote-First Culture: Flexible work environment with team members distributed globally. We have two hubs; New York and Melbourne with optional hybrid work if desired.

Cutting-Edge Domain: Work at the intersection of AI and decentralized systems, tackling some of the most challenging engineering problems in what is about to be one of the largest intersections of two previously non-overlapping fields ever.

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 monopolizing model development, access and release, and achieving massive economic capture. If this resonates, please apply.