
Artificial Intelligence • Web 3
Pluralis Research is a foundational AI research lab focused on Protocol Learning — decentralized, multi‑participant training of foundation models where no single participant holds a full copy of the model. The group develops methods to enable communication‑efficient model and pipeline parallelism, unextractable collaborative models, and high‑compression context parallelism so community‑trained, community‑owned frontier models can scale over low‑bandwidth, internet‑connected devices. Their work targets practical systems and algorithms that make decentralized training competitive with centralized training while enabling new ownership and economic models.
November 6

Artificial Intelligence • Web 3
Pluralis Research is a foundational AI research lab focused on Protocol Learning — decentralized, multi‑participant training of foundation models where no single participant holds a full copy of the model. The group develops methods to enable communication‑efficient model and pipeline parallelism, unextractable collaborative models, and high‑compression context parallelism so community‑trained, community‑owned frontier models can scale over low‑bandwidth, internet‑connected devices. Their work targets practical systems and algorithms that make decentralized training competitive with centralized training while enabling new ownership and economic models.
• Design resource management systems provisioning and orchestrating compute across AWS, GCP, and Azure using infrastructure-as-code. • Architect fault-tolerant infrastructure for distributed ML. GPU clusters, health monitoring, and resilient retry strategies. • Build systems that simulate and handle real-world network conditions while managing dynamic node churn.
• 5+ years of work experience with deep experience in: • Infrastructure-as-Code: Production Pulumi/Terraform/CloudFormation managing multi-cloud deployments. • Python Engineering: Idiomatic async Python with error handling, retry logic, concurrent execution. • Container & GPU: Docker, Kubernetes/EKS, GPU workloads, heterogeneous clusters. • Networking: Decentralized topologies and routing, NAT hole punching, P2P multi-address coordination. • ML Infrastructure: Distributed training workflows, checkpoint management, data sharding. • Observability & SRE: Monitoring systems (Prometheus/Grafana), incident response, performance optimization. • Experience in a startup environment with an emphasis on micro-services orchestration or big tech background. • A team player with high attention to detail. • A strong passion to work at the intersection of AI and decentralized systems.
• Health insurance • Professional development support
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