Machine Learning Engineer

Machine Learning Engineer

As an ML Engineer at Pluralis, you'll implement and optimize low-bandwidth model-parallel training systems that enable truly distributed language model development. Your work will directly contribute to creating a more open AI ecosystem where anyone can participate in frontier model development, not just large corporations with massive compute resources.

Key Responsibilities

• Distributed Training Implementation : Build and optimize systems for training large models across heterogeneous hardware connected by low-bandwidth networks.
• Performance Optimization : Implement techniques to reduce communication overhead while maintaining model convergence in challenging network environments.
• Training Infrastructure : Design and develop robust training pipelines that can recover from node failures and network disruptions.
• Model Serving : Create efficient systems for deploying sharded models in a protocol-locked environment.
• Metrics & Monitoring : Develop tools to track training progress, evaluate model quality, and identify bottlenecks in distributed environments.

What We're Looking For

Compensation & Benefits

Backed by Union Square Ventures and other tier-1 investors, we’re a world‑class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We view the world as a better place if we are able to implement what we are attempting, and Protocol Learning as the only plausible approach to preventing a handful of massive corporations monopolising model development, access and release, and achieving massive economic capture. If this resonates, please apply.

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