Research Scientist – Geo-Distributed Inference

🕒 October 14

🌐 United States, Australia – Remote

🇺🇸 United States – Remote 🇦🇺 Australia – Remote

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

👻 Ghost score 25%

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Pluralis Research

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

🌐 Web 3

Artificial Intelligence • Web 3

Pluralis Research is a foundational AI research lab focused on Protocol Learning — decentralized, multi‑participant training of foundation models where no single participant holds a full copy of the model. The group develops methods to enable communication‑efficient model and pipeline parallelism, unextractable collaborative models, and high‑compression context parallelism so community‑trained, community‑owned frontier models can scale over low‑bandwidth, internet‑connected devices. Their work targets practical systems and algorithms that make decentralized training competitive with centralized training while enabling new ownership and economic models.

📋 Description

• Build and own the inference stack end-to-end • Set direction for pipeline-parallel execution, placement and routing, transport, serving engine, and failure handling • Invent, design, validate, and productionize algorithms for fast inference on consumer hardware over the public internet • Keep the rollout pipeline fast and reliable for reinforcement learning training • Develop the serving layer for models once trained • Support inference across Macs and consumer GPUs owned by distributed participants • Handle nodes joining and leaving mid-run and model weights changing during training

🎯 Requirements

• Experience shipping serving-engine internals or building a large-scale inference system • Hands-on ability to work on serving systems • Publications in distributed inference or a nearby field, such as LLM serving systems, pipeline parallelism over slow networks, or decentralized training, or equivalent unpublished work that can be discussed • Experience with low-bandwidth, high-latency systems operating over the public internet • Belief in Protocol Learning as a viable approach for collective, trustless, and sovereign AI • Familiarity with RL post-training • Exposure to Apple silicon or MLX • Experience with P2P networking and NAT traversal • Experience at proprietary, open-weight, or open-source AI labs • Professional-level English proficiency, written and spoken • Ability to work across timezones

🏖️ Benefits

• Significant equity ownership for key technical contributors in addition to a high base salary • Flexible work environment • Optional full visa sponsorship • Relocation support to Australia or the US • Opportunity to work on open, frontier research problems • Remote-first culture • Globally distributed team

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