[Hiring] Research Engineer @Pluralis Research

Role Description

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.

Qualifications

• 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.

Requirements

• 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.

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.