About the role

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 https://arxiv.org/abs/2607.13332). 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 https://pluralis.ai/blog/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 https://arxiv.org/abs/2506.01260, Factored Gossip DiLoCo https://arxiv.org/abs/2606.22768, AsyncMesh https://arxiv.org/abs/2601.22442, and Sentinel https://arxiv.org/abs/2603.03592. 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. We are backed by Union Square Ventures https://www.usv.com/ and other tier-1 investors, and we are a world-cl