Research Scientist - Pre-training

Pluralis Research • San Francisco, CA

1d • Remote

About The Position

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 Scientist 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.

Requirements

• You've trained models across many devices in PyTorch with FSDP, DeepSpeed, Megatron, or your own implementation. You understand data, tensor, and pipeline parallelism.
• Production-quality Python. Concurrency, failure handling, profiling before optimizing.
• Shipped systems, research code, open-source work, or serious personal projects.
• You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI.
• Professional-level English proficiency (written and spoken).

Nice To Haves

• 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

Responsibilities

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

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed