Research Engineer – Geo-Distributed Inference

Job not on LinkedIn

🔥 15 hours ago

🌐 United States, Australia – Remote

🇺🇸 United States – Remote 🇦🇺 Australia – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📚 Research Engineer

👻 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 on it • 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 • Develop the serving layer for models once they are trained

🎯 Requirements

• Experience shipping serving-engine internals or independently building a large-scale inference system • Hands-on ability to perform this work today • Publications in distributed inference or a nearby field, such as LLM serving systems, pipeline parallelism over slow networks, or decentralized training, or unpublished work that can be explained • Experience with systems operating in low-bandwidth, high-latency settings such as the public internet • Belief that Protocol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 timezones

🏖️ 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 and relocation support to either Australia or the US