Teams at Pluralis Research

Build and ship a production project for Agora, a decentralized distributed training system. Develop multiprocessing, asynchronous I/O, and threading components; work with large-scale training infrastructure across AWS and GCP; contribute daily to production code through reviews and mentorship; and present final project results to engineering and research teams.

Conduct novel research in Protocol Learning, focusing on decentralized large-model training and inference across consumer devices. Develop foundational methods addressing communication efficiency, fault tolerance, heterogeneous compute, and malicious participants, with the goal of publishing in tier-1 machine learning venues. Interns own a research problem, leverage significant compute, and receive mentorship from senior scientists during a six-month PhD internship.

Build an end-to-end decentralized RL post-training stack for large language models, including rollout ingestion, reward computation, policy updates, weight synchronization, and evaluation. Develop algorithms for asynchronous, high-latency, partially trusted environments and ship post-trained models as public artifacts. The role requires hands-on RLHF, RLVR, or reasoning-focused RL experience, strong Python and PyTorch engineering, and research capability. Experience with slow networks, decentralized systems, model serving, reward modeling, or P2P networking is advantageous.