Research Scientist - Geo-Distributed Inference

The role

You will build and own the end-to-end inference stack, including pipeline-parallel execution, placement, routing, transport, and failure handling. You will design and validate new algorithms to make inference fast on consumer hardware over the public internet. The role involves serving the rollout pipeline for RL training and later serving trained models.

What you will do

• Own the inference stack end-to-end including pipeline-parallel execution, placement, routing, transport, serving engine, and failure handling.
• Design, validate, and put into production new algorithms for fast inference on consumer hardware over the public internet.
• Keep the rollout pipeline fast and reliable for RL training.
• Turn the pipeline into the serving layer for models once they are trained.

Required qualifications

• Shipped serving-engine internals or built a large-scale inference system hands-on.
• Research ability through publications in distributed inference or nearby fields like LLM serving systems, pipeline parallelism over slow networks, or decentralized training, or unpublished work.
• Experience with systems that run in low-bandwidth, high-latency settings like the public internet.
• Belief that Protocol Learning is the viable third path for collective, trustless, and sovereign AI.
• Professional-level English proficiency (written and spoken).

Nice to have

• Familiarity with RL post-training.
• Exposure to Apple silicon or MLX.
• Experience with P2P networking and NAT traversal.
• Experience at proprietary, open-weight and open-source AI labs

Benefits

• Equity-Heavy Package including significant ownership and a high base salary.
• Remote-First Culture.
• Optional full visa sponsorship and relocation support to either Australia or the US.

Before you apply

• Optional full visa sponsorship and relocation support to either Australia or the US is available.

About the company

Pluralis Research works on Protocol Learning to train and serve large models in a fully decentralized way on small consumer-grade devices. They are a world-class, deeply technical team of ML researchers backed by Union Square Ventures and other tier-1 investors.