As a Machine Learning Engineer, you'll design and implement distributed systems for training large-scale ML models while optimizing performance under challenging conditions.

The Developer Relations Lead will bridge Pluralis's research and the ML community by creating technical content, engaging in community discussions, representing at conferences, and improving contributor experience while tracking metrics for growth.

The Research Scientist will tackle advanced research problems in Protocol Learning, publish findings, and work collaboratively in a distributed model training environment.

Pluralis Research invites established researchers for collaboration on decentralized AI through joint publications and shared insights, focusing on model-parallel training.

Conduct research in Protocol Learning, publish findings, and engage with distributed machine learning on consumer-grade devices. Transform ideas into foundational papers.

The Research Scientist Intern will conduct novel research in Protocol Learning, aimed at publishing in top-tier machine learning conferences, and will gain mentorship from senior scientists.

The Developer Relations Lead will bridge Pluralis's research and the ML community, creating accessible content and engaging with researchers. Responsibilities include producing technical materials, enhancing contributor experiences, and representing Pluralis at conferences.

Design and implement robust, large-scale distributed ML training systems optimized for low-bandwidth, high-latency environments. Build model-parallel training strategies, checkpointing and recovery, GPU and memory optimizations, P2P networking, NAT traversal, and monitoring to ensure resilient, efficient multi-participant training.