Research Scientist Intern

Reposted 4 Days Ago
Be an Early Applicant
Hiring Remotely in Australia
Remote
Internship
Artificial Intelligence • Information Technology • Software
The Role
Contribute to research in Protocol Learning while pursuing a PhD, aiming for publication in top machine learning conferences. The role includes mentorship from senior scientists.
Summary Generated by Built In

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.

Key Responsibilities
  • 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).

What We're Looking For
  • 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.

Top Skills

PyTorch
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The Company
15 Employees

What We Do

Pluralis is developing a protocol that facilitates collaborative training and ownership of foundation models.

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