Research Engineer – Decentralized Training and Inference Verification

🕒 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

• Enumerate and maintain the threat model for malicious or careless workers across pre-training, post-training, and inference • Account for training disruption, denial-of-service, free-riding, model poisoning, backdoors, data extraction, reputation manipulation, and reward manipulation • Build statistical verification methods with stated error rates • Tune verification tests using rigorous benchmarks • Control false positives and false negatives across heterogeneous hardware, including different GPUs and Macs • Build and operate the verification service in the inference path • Take responsibility for verifier performance and mistakes • Develop efficient algorithms and systems to verify that tokens came from the claimed model and sampling parameters • Verify that training contributions are what participants claim them to be

🎯 Requirements

• Experience building a calibrated statistical decision system with stated error rates and living with its mistakes • Publications in inference and training verification, or equivalent experience in fraud detection, anti-cheat, or experimentation platforms • Deep expertise in statistics and probability • Ability to design experiments, calibrate decision thresholds, and defend claimed error rates • Knowledge of verifying untrusted compute, including statistical testing, re-execution, cryptographic proofs, and trusted hardware • Ability to assess what verification approaches fit a permissionless network • Belief that Protocol Learning is viable for collective, trustless, and sovereign AI • Familiarity with large-scale pre-training and RL post-training • Familiarity with decentralized ML security and adversarial threat models, including poisoning, Sybil, collusion, and replay • Experience at proprietary, open-weight, or open-source AI labs • Professional-level English proficiency, written and spoken

🏖️ 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