Research Engineer - Post-Training

Pluralis Research develops Protocol Learning, training and serving large models in a decentralized manner across consumer-grade devices connected over the internet. The Research Engineer - Post-Training will make reinforcement-learning post-training work in this environment by building the end-to-end training stack, adapting algorithms for asynchronous and high-latency conditions, and releasing the first decentralized post-trained models.

Responsibilities:

• Build the post-training stack: You build the RL training loop end-to-end: rollout ingestion from the geo-distributed inference pipeline, reward computation, policy updates, and getting updated weights back out to the network. You set the direction, and you make things happen
• Invent the algorithms: Standard RL recipes assume on-policy rollouts from fast, trusted hardware. You adapt them to asynchronous, high-latency, partially trusted generation: staleness tolerance, off-policy corrections, and communication-efficient policy updates
• Ship first post-trained models: You build the evals that show the models are improving, and you take the first decentralized post-trained release from run to public artifact