Machine Learning Engineer – Distributed ML Systems

🕒 April 1

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Pluralis Research

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

🌐 Web 3

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.

📋 Description

• 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. • Architect resilient training systems where nodes can fail, networks can partition, and participants can dynamically join or leave. • 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.

🎯 Requirements

• Strong experience building and operating distributed systems in production • Hands-on expertise with distributed training frameworks (FSDP, DeepSpeed, Megatron, or similar) • Deep understanding of model parallelism (data, tensor, pipeline parallelism) • Expert-level Python with production experience (concurrency, error handling, retry logic, clean architecture) • Strong networking fundamentals: P2P systems, gRPC, routing, NAT traversal, distributed coordination • Experience optimizing GPU workloads, memory management, and large-scale compute efficiency

🏖️ Benefits

• Equity-heavy compensation with meaningful ownership in a mission-driven company • Competitive base salary for senior engineering roles in Australia • Visa sponsorship available for exceptional candidates • Remote-first with optional access to our Melbourne hub • World-class team — team mates were previously at at Google, Amazon, Microsoft, and leading startups