Pluralis Research

Pluralis Research

Machine Learning Engineer - Distributed ML Systems

San Francisco · Staff+

H1B sponsorship availableDetected 112 days ago
PythonDistributed SystemsgRPCMachine LearningResearchCommunication

About the role

  • Pluralis Research https://pluralis.ai/ carries out foundational research on Protocol Learning: multi-participant training of foundation models where no single participant has, or can ever obtain, a full copy of the model.
  • The purpose of Protocol Learning is to facilitate the creation of community-trained and community-owned frontier models with self-sustaining economics.
  • You'll be implementing a novel substrate for training distributed ML models that work under consumer grade internet connection.

Responsibilities

  • Design and implement large-scale distributed training systems optimized for heterogeneous hardware operating under low-bandwidth, high-latency conditions.
  • Develop and optimize model-parallel training strategies (data, tensor, pipeline parallelism) with custom sharding techniques that minimize communication overhead.
  • Optimize GPU utilization, memory efficiency, and compute performance across distributed nodes.
  • Implement robust checkpointing, state synchronization, and recovery mechanisms for long-running, fault-prone training jobs.
  • Build monitoring and metrics systems to track training progress, model quality, and system bottlenecks.
  • Design and optimize peer-to-peer topologies for decentralized coordination across non-co-located nodes.
  • Implement NAT traversal, peer discovery, dynamic routing, and connection lifecycle management.
  • Profile and optimize communication patterns to reduce latency and bandwidth overhead in multi-participant environments.
  • Strong experience building and operating distributed systems in production.

Compensation

  • Competitive base salary for senior engineering roles in Australia

Benefits

  • Equity-heavy compensation with meaningful ownership in a mission-driven company
  • Architect resilient training systems where nodes can fail, networks can partition, and participants can dynamically join or leave.

Visa & Work Authorization

  • Visa sponsorship available for exceptional candidates

This listing is sourced directly from Pluralis Research's careers page and normalized into a canonical job model.