Research Engineer - Decentralized Training and Inference Verification
AI
Pluralis Research is seeking a Research Engineer to design and build verification systems for decentralized training and inference on consumer-grade GPUs. The role focuses on statistical verification methods, threat modeling, and shipping a verifier in the inference path. Candidates need deep statistical expertise and experience with untrusted compute verification.
What you'll do
- Own the threat model: 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 keep that model current as the network grows.
- Design and calibrate the tests: 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: build and run the verification service in the inference path, and live with its mistakes.
What you need
Must-have
StatisticsProbabilityStatistical verification methodsThreat modelingCryptographic proofsTrusted hardwareRe-executionMachine LearningDecentralized ML security
Nice-to-have
Large scale Pre-trainingRL post-trainingAdversarial threat modelsPoisoningSybilCollusionReplay
Soft skills
Mission alignmentComfortable working across timezonesProfessional-level English proficiency
Research EngineerDecentralized TrainingInference VerificationProtocol LearningStatisticsMachine LearningRemoteFull-timeAustraliaTechnology