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Research Scientist in Distributed AI Training (Protocol Learning)

pluralis research • United State
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AI Summary

Lead the development of scalable, fault-tolerant distributed training systems for frontier AI models across heterogeneous hardware and low-bandwidth networks. Implement cutting-edge techniques in model-parallelism, communication efficiency, and system resilience to advance Protocol Learning. Collaborate with a globally distributed team to solve open challenges in decentralized AI infrastructure.

Key Highlights
Pioneer distributed AI training systems for frontier models on consumer-grade hardware over the internet.
Focus on model-parallelism, tensor/pipeline parallelism, and communication-efficient training under heterogeneous and unreliable conditions.
Build fault-tolerant, elastic systems with robust checkpointing and state synchronization for dynamic node participation.

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Key Responsibilities
Develop and optimize model-parallel training systems for large-scale AI models across heterogeneous GPUs.
Implement communication-efficient techniques to reduce overhead in low-bandwidth, high-latency network environments.
Design fault-tolerant mechanisms for elastic training runs, including robust checkpointing and state synchronization.
Build monitoring and instrumentation tools to track system performance, bottlenecks, and model quality across distributed devices.
Technical Skills Required
Distributed Systems Machine Learning (Deep Learning) Python

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Benefits & Perks
Equity-heavy compensation package
Remote-first culture with global team
Optional full visa sponsorship and relocation support to Australia or the US

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Nice to Have
Experience training or serving large language models (e.g., Nemotron, Qwen, OLMo)
Hands-on experience with peer-to-peer (P2P) networking and NAT traversal
Post-training and reinforcement learning (RL) experience
Experience with inference and serving systems for AI models

Job Description

Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. Despite being dismissed as infeasible, we have made significant advances on this problem, most recently Agora, a permissionless run that pretrained an 8B model from scratch on consumer GPUs spread over the internet, with no single participant ever holding the full weights (tech report). While many of the core research problems have been solved, Protocol Learning unlocks a series of new challenges. For the mission in full, read A Third Path: Protocol Learning.
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Job Overview
Posted Date Oct 14, 2026
Employment Type Full-time
Experience Level Not Applicable
Location United State
Category Programming
Company pluralis research
Mentioned Skills
python ai fault tolerance nat pytorch deepspeed

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