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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.

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

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.