Teams at Pluralis Research

Develop end-to-end reinforcement learning post-training for large language models operating across decentralized, high-latency consumer hardware. Responsibilities include building rollout ingestion, reward computation, policy updates, weight synchronization, asynchronous and off-policy algorithms, evaluation systems, and public model releases. The role requires strong Python and PyTorch engineering, hands-on RLHF/RLVR experience, and research ability in distributed or asynchronous reinforcement learning.

Develop efficient algorithms and systems for verifying untrusted decentralized training and inference. Responsibilities include modeling threats such as poisoning, free-riding, data extraction, and reward manipulation; designing statistically calibrated tests with controlled error rates across heterogeneous hardware; and deploying verification services in production inference systems.

Build and optimize decentralized distributed pretraining systems for frontier-scale models across heterogeneous consumer GPUs and unreliable internet connections. Responsibilities include model, data, and pipeline parallelism; communication optimization; elasticity and fault tolerance; checkpointing and recovery; and monitoring throughput, bottlenecks, and model quality. The role requires hands-on PyTorch distributed training experience, strong production Python engineering, and demonstrated execution through shipped systems, research code, open-source work, or substantial projects.