Research Scientist - Decentralized Training and Inference Verification

The role

You will develop efficient algorithms and systems to verify work in a decentralized training and inference network. This includes ensuring tokens come from claimed models and that training contributions are accurate.

What you will do

• Own the threat model by enumerating malicious or careless worker actions across pre-training, post-training, and inference.
• Design and calibrate statistical verification methods with stated error rates.
• Tune tests with rigorous benchmarks and control false positives and negatives across heterogeneous hardware.
• Build and run the verification service in the inference path.

Required qualifications

• Built a calibrated statistical decision system with stated error rates.
• Deep expertise in statistics and probability.
• Ability to design experiments, calibrate decision thresholds, and defend error rates.
• Knowledge of the solution space for verifying untrusted compute, including statistical testing, re-execution, cryptographic proofs, and trusted hardware.
• Professional-level English proficiency (written and spoken).

Nice to have

• 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

Benefits

• Equity-Heavy Package: significant ownership for key technical contributors in addition to a high base salary.
• Remote-First Culture: Flexible work environment with team members distributed globally.
• Optional full visa sponsorship and relocation support to either Australia or the US.

About the company

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. They are a world-class, deeply technical team of ML researchers backed by Union Square Ventures and other tier-1 investors.