Research Engineer - Decentralized Training and Inference Verification

Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. Despite being dismissed as infeasible, we have made significant advances on this problem, most recently Agora, a permissionless run that pretrained an 8B model from scratch on consumer GPUs spread over the internet, with no single participant ever holding the full weights (tech report).

While many of the core research problems have been solved, Protocol Learning unlocks a series of new challenges. For the mission in full, read A Third Path:
Protocol Learning.

Our training and inference network is trustless, and the workers are GPUs scattered across the world. Many things can go wrong in this system. An inference worker can return tokens from a cheaper model, or a highly quantized version of the one it's supposed to run. Even with the right model, it can sample with the wrong parameters. Training faces its own attacks, and Agora showed what a permissionless run deals with in practice: participants who disrupt training by dropping updates or flooding the system, free-riders who submit trivial work and collect the rewards, poisoned updates that plant backdoors, attempts to extract private data from gradients and activations, and contributors who inflate their reported work to claim rewards they didn't earn.

Sentinel is our first published answer on the training side. Your primary role is to come up with efficient algorithms and systems that verify the work: that tokens came from the claimed model and sampling parameters, and that training contributions are what they claim to be.

• Own the threat model
  :
  You enumerate what a malicious or careless worker can do across pre-training, post-training, and inference — training disruption and denial-of-service, free-riding, model poisoning and backdoors, data extraction from gradients and activations, reputation and reward manipulation — and you keep that model current as the network grows.

• Design and calibrate the tests
  :
  You build statistical verification methods with stated error rates, tune them with rigorous benchmarks, and keep false positives and false negatives controlled across heterogeneous hardware, including different GPUs and Macs.

• Ship the verifier
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  You build and run the verification service in the inference path, and you live with its mistakes.

• Verification systems, shipped or published
  :
  You've built a calibrated statistical decision system with stated error rates and lived with its mistakes. Publications in inference and training verification count; fraud detection, anti-cheat, and experimentation platforms count as much as papers do.

• Statistical depth
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  Deep expertise in statistics and probability, with the ability to design experiments, calibrate decision thresholds, and defend the error rates you claim.

• Technical background
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  You know the solution space for verifying untrusted compute, from statistical testing to re-execution, cryptographic proofs, and trusted hardware, and you can argue what fits a permissionless network and what doesn't.

• Mission alignment
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  You believe Protocol Learning is the viable third path 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, such as poisoning, Sybil, collusion, and replay.

• Experience at proprietary, open-weight and open-source AI labs

• Equity-Heavy Package
  :
  We offer significant ownership for key technical contributors in addition to a high base salary.

• Remote-First Culture
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  Flexible work environment with team members distributed…