Research Scientist Intern
Pluralis Research is a research lab developing decentralized AI through Protocol Learning, a communication-efficient approach to collaborative model training. It operates open training systems that allow distributed contributors to provide compute for collectively owned foundation models.
Funding history
About Pluralis Research
Pluralis Research develops Protocol Learning, which enables foundation models to be trained and served across globally distributed participants without requiring a single participant to hold the complete model. Its work covers low-bandwidth model parallelism, asynchronous distributed optimization, fault-tolerant training, privacy-preserving unextractable models, and collective ownership. The organization operates Agora and Node0 training systems, publishes research, provides participation documentation, and releases open-source software for distributed training and reinforcement-learning workflows.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will conduct novel research on decentralized training challenges and aim to publish at leading ML conferences. You will select a foundational scaling problem, investigate it during a fixed-term PhD internship, and use large-scale training infrastructure to develop your results.
Requirements
- Current PhD candidate status
- At least one publication in NeurIPS, ICML, ICLR, or another top-tier ML venue
- Research focus in a technical area relevant to frontier models
- Strong theoretical understanding of deep learning and distributed systems
- PyTorch proficiency
- Experience with large-scale training infrastructure
- Professional-level written and spoken English
Responsibilities
- Conduct novel research in Protocol Learning
- Publish research in tier-1 ML conferences
- Select and answer a scaling problem affecting Protocol Learning