Research Engineer – Post-Training

🕒 August 31

🌐 United States, Australia – Remote

🇺🇸 United States – Remote 🇦🇺 Australia – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📚 Research Engineer

👻 Ghost score 34%

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 the RL post-training stack end-to-end, including rollout ingestion, reward computation, policy updates, and distributing updated weights across the network • Set the direction for the post-training stack and execute its development • Adapt RL algorithms to asynchronous, high-latency, partially trusted generation • Address staleness tolerance, off-policy corrections, and communication-efficient policy updates • Build evaluations demonstrating model improvement • Deliver the first decentralized post-trained model release as a public artifact

🎯 Requirements

• Hands-on experience running RL post-training on large language models, including RLHF, RLVR, or reasoning-focused RL • Experience with rollout generation, asynchronous training loops, and weight synchronization • Production-quality Python and PyTorch skills • Experience with concurrency, failure handling, and profiling before optimizing • Publications in RL post-training, asynchronous or distributed RL, or nearby fields, or unpublished work that can be defended in detail • Belief in Protocol Learning as a viable path for collective, trustless, and sovereign AI • Professional-level English proficiency, written and spoken • Comfort working across timezones • Nice to have: experience training over slow networks or with decentralized/federated setups • Nice to have: familiarity with vLLM or SGLang serving internals • Nice to have: experience with reward modeling or verifiable-reward datasets • Nice to have: experience with P2P networking and NAT traversal • Nice to have: experience at proprietary, open-weight, and open-source AI labs

🏖️ 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