Senior/Staff Machine Learning Engineer - Distributed Systems

torentify • United States

Remote Visa Sponsorship

This Job is No Longer Active This position is no longer accepting applications

AI Summary

Design and implement large-scale distributed training systems for heterogeneous hardware under low-bandwidth conditions. Develop resilient peer-to-peer networking architectures and optimize model parallelism strategies for decentralized ML. Requires 5+ years of experience in distributed systems, large-scale ML training, and production Python.

Key Highlights

Build novel technical foundation for training distributed ML models over consumer-grade internet connections.

Architect resilient systems supporting dynamic node joining/leaving and operating despite network partitions.

Optimize GPU utilization, memory efficiency, and communication overhead across distributed nodes.

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Key Responsibilities

Design and implement large-scale distributed training systems for heterogeneous hardware operating under low-bandwidth and high-latency network conditions.

Develop and optimize model-parallel training strategies, including data, tensor, and pipeline parallelism.

Implement custom sharding techniques designed to minimize communication overhead.

Optimize GPU utilization, memory efficiency, and compute performance across distributed nodes.

Build robust checkpointing, state synchronization, and recovery mechanisms for long-running and fault-prone training jobs.

Develop monitoring and metrics systems to track training progress, model quality, and system bottlenecks.

Architect resilient distributed training systems that can operate despite node failures and network partitions.

Support systems where participants can dynamically join or leave the network.

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 usage across multi-participant environments.

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Technical Skills Required

Distributed Systems Python Machine Learning

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Benefits & Perks

Equity-heavy compensation

Visa sponsorship available for exceptional candidates

Remote-first work with optional access to the Melbourne hub

Job Description

## About the Company

Pluralis Research conducts foundational research into Protocol Learning, a distributed approach to training foundation models in which no single participant has or can obtain a complete copy of the model.

The company is focused on enabling community-trained and community-owned frontier models with self-sustaining economics. Pluralis Research is backed by Union Square Ventures and other tier-1 investors and brings together ML researchers and engineers with experience from leading technology companies and startups.

## About the Role

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