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Pluralis Research
Pluralis Research
4 open remote positions
Pluralis Research is a decentralized AI research company developing Protocol Learning to train large models on consumer devices across the internet. The team is world-class and deeply technical, backed by tier-1 investors, with a remote-first culture.
Benefits Overview
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Open Positions
Distributed pretraining: Implement and optimize model-parallel training on heterogeneous GPUs under low-bandwidth, high-latency links. Performance optimization: Apply techniques that reduce communication overhead while maintaining model convergence in challenging network environments. Elasticity and fault tolerance: Build robust checkpointing and recovery so runs survive node churn as participants join and leave.
PyTorch
Distributed Training
Python
Own the threat model for decentralized training and inference, enumerating potential attacks and keeping it current. Design and calibrate statistical verification methods with stated error rates across heterogeneous hardware. Build and run the verification service in the inference path and take responsibility for its mistakes.
Statistics
Machine Learning
Cryptography
Own the inference stack end-to-end, including pipeline-parallel execution, placement and routing, transport, serving engine, and failure handling. Invent and validate novel algorithms for fast inference on consumer hardware over the public internet, then put them into production. Serve both RL training rollouts and final model serving in a permissionless, trustless setting.
Distributed Systems
Build the RL post-training stack end-to-end: rollout ingestion, reward computation, policy updates, and returning updated weights to the network. Invent algorithms for asynchronous, high-latency, partially trusted generation, including staleness tolerance and off-policy corrections. Ship the first decentralized post-trained models, building evals and taking the release from run to public artifact.
Python
PyTorch
Reinforcement Learning
Distributed Systems
VLLM