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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.

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.

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.