Teams at Pluralis Research

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.

Conduct foundational research on Protocol Learning, focusing on communication-efficient decentralized training, convergence under churn and staleness, heterogeneous systems, and malicious-participant robustness. Publish findings in top-tier conferences and collaborate with engineering teams to deploy methods in live frontier-model training runs. The role requires machine learning research excellence, distributed training experience, strong PyTorch skills, and alignment with decentralized AI.