Research Engineer - Pre-training

Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. Despite being dismissed as infeasible, we have made significant advances on this problem, most recently Agora, a permissionless run that pretrained an 8B model from scratch on consumer GPUs spread over the internet, with no single participant ever holding the full weights ([tech report]( While many of the core research problems have been solved, Protocol Learning unlocks a series of new challenges. For the mission in full, read [A Third Path: Protocol Learning](

This setting breaks nearly every assumption of datacenter training: communication-efficient training across different parallelism axes, fault tolerance as nodes join and drop mid-run, heterogeneous compute and networks, and robustness to malicious participants. Our published methods include [Subspace Networks]( [Factored Gossip DiLoCo]( [AsyncMesh]( and [Sentinel](

As a Research Engineer you'll build the training system that takes Protocol Learning from the 8B run to frontier scale: large models on heterogeneous hardware, in physically different regions, connected by ordinary internet.

Key Responsibilities

• Distributed pretraining : Implement and optimize model-parallel training. Data, pipeline, and tensor parallelism for large models on heterogeneous GPUs under low-bandwidth, high-latency links.
• Performance optimization : Implement techniques that reduce communication overhead while maintaining model convergence in challenging network environments.
• Elasticity and fault tolerance : Make runs survive node churn. Robust checkpointing, state synchronization, and recovery as participants join and leave.
• Run instrumentation : Build the monitoring that shows throughput, bottlenecks, and model quality across hundreds of devices.

What We're Looking For

• Hands-on distributed training (required) : You've trained models across many devices in PyTorch with FSDP, DeepSpeed, Megatron, or your own implementation. You understand data, tensor, and pipeline parallelism.
• Strong engineering : Production-quality Python. Concurrency, failure handling, profiling before optimizing.
• Evidence of execution : Shipped systems, research code, open-source work, or serious personal projects.
• Mission alignment : You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI.

Nice to Have

• Hands-on experience training or serving large language models such as Nemotron, Qwen or OLMo.
• Experience with P2P networking and NAT traversal.
• Experience with post-training and RL.
• Experience with inference and serving systems.
• Experience at proprietary, open-weight and open-source AI labs

Compensation & Benefits

• Equity-Heavy Package : We offer significant ownership for key technical contributors in addition to a high base salary.
• Remote-First Culture : Flexible work environment with team members distributed globally.
• Visa Sponsorship : Optional full visa sponsorship and relocation support to either Australia or the US.
• Open Problems : Training and serving frontier models on hardware you don't control, over networks you don't own, mostly has no published answers yet. You'll write some of the first ones.

FYI's

• We work remotely across the world, with the main teams in Australia and North America. You'll need to be comfortable working across timezones.
• Applicants must have professional-level English proficiency (written and spoken).
• Recruiters: we aren't looking for agency support at this time. We'll reach out if we need help.