Research Scientist - Geo-Distributed Inference

Pluralis Research; United States; Remote; AUD 180,000 - 280,000; Full time

Benefits offered by this job

Equity-Heavy Package

Remote-First Culture

Visa Sponsorship

Job summary

Pluralis Research is hiring to advance Protocol Learning, training and serving large models on consumer devices over the internet. You will own the end-to-end inference stack, design new algorithms for fast deployment, and keep RL training pipelines efficient as they transition to serving for trained models.

Ideal candidates have shipped serving systems, strong research credentials, and experience with low-bandwidth networks.

Qualifications

• Publications or blog posts in distributed inference or nearby fields are valued.
• Experience with LR/LLM serving systems and large-scale inference is preferred.
• Strong English communication and research presentation skills.

Responsibilities

• Own the inference stack end-to-end: planning, placement, transport, serving engine and failure handling.
• Invent algorithms to speed inference on consumer hardware over the internet and deploy them.
• Maintain rollout pipelines for RL training and convert them to serving for trained models.

Skills

Shipped serving systems

Research ability

Low-bandwidth networking

Mission alignment

Job description

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.

Key Responsibilities

• Own the inference stack: You build and own it end-to-end. Pipeline-parallel execution, placement and routing, the transport, the serving engine, and failure handling. You set the direction, and you make things happen.
• Invent the algorithms: Making inference fast on consumer hardware over the public internet takes methods that don't exist yet. You design them, validate them, and put them in production.
• Serve training and users: You keep the rollout pipeline fast and reliable for RL training now, and turn it into the serving layer for our models once they're trained.

What We're Looking For

• Shipped serving systems: You've shipped serving-engine internals or built a large-scale inference system yourself, and you can do this work hands-on today.
• Research ability: Publications (papers and blogposts) in distributed inference or a nearby field, such as LLM serving systems, pipeline parallelism over slow networks, or decentralized training, are a strong signal. So is unpublished work you can walk us through.
• Low-bandwidth networking: Experience with systems that run in low-bandwidth, high-latency settings like the public internet is a strong signal.
• Mission alignment: You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI.

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.

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).

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.