Research Scientist – Geo-Distributed Inference

🔥 16 hours ago

🌐 United States, Australia – Remote

🇺🇸 United States – Remote 🇦🇺 Australia – Remote

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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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, including pipeline-parallel execution, placement and routing, transport, serving engine, and failure handling • Set the technical direction for the inference stack and execute it hands-on • Design, validate, and productionize new algorithms for fast inference on consumer hardware over the public internet • Keep the rollout pipeline fast and reliable for reinforcement-learning training • Turn the rollout pipeline into the serving layer for trained models • Work on systems operating across Macs and consumer GPUs owned by strangers, public-internet networks, dynamically joining and leaving nodes, and changing model weights

🎯 Requirements

• Shipped serving-engine internals or built a large-scale inference system hands-on • Research ability in distributed inference or a nearby field, demonstrated by publications or unpublished work • Experience with systems operating in low-bandwidth, high-latency settings such as the public internet • Belief that Protocol Learning is a viable path 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, and open-source AI labs • Professional-level English proficiency, written and spoken • Comfortable working across time zones

🏖️ Benefits

• Significant equity ownership for key technical contributors in addition to a high base salary • Flexible work environment with team members distributed globally • Optional full visa sponsorship • Relocation support to Australia or the US • Opportunity to work on open research problems in training and serving frontier models