Research Engineer - Pre-training

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.

We are backed by Union Square Ventures and other tier-1 investors, and we are a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We believe AI, and the world, end up on a better path if we succeed in implementing the protocol for intelligence. If this resonates, please apply.