Teams at Pluralis Research

Architect and build a multi-cloud ML training platform for decentralized large-model training and inference across consumer devices and cloud instances. Responsibilities include infrastructure orchestration, distributed compute, GPU workloads, checkpointing, data streaming, network-condition simulation, node-churn management, observability, reliability, and resilient job execution. The role requires strong Python, infrastructure-as-code, Kubernetes, distributed systems, decentralized networking, and SRE experience.

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.