Research Engineer: RL Post-Training & Decentralized Models

Pluralis Research; United States; Remote; USD 170,000 - 230,000; Full time

Job summary

Pluralis Research is seeking an AI engineer to advance reinforcement learning systems. You will build the RL post-training stack end-to-end, from rollout ingestion to reward computation and policy updates, and you will implement robust monitoring and versioning to keep models auditable.

You will invent algorithms for asynchronous, high-latency, partially trusted generation, including staleness tolerance and off-policy corrections, and you will design experiments that validate improvements under

Responsibilities

• Build the RL post-training stack end-to-end, from rollout ingestion to reward computation and policy updates, with robust monitoring and versioning for auditable models.
• Invent algorithms for asynchronous, high-latency, partially trusted generation, including staleness tolerance and off-policy corrections, and design experiments under production constraints to validate improvements.
• Ship the first decentralized post-trained models, building evals and taking the release from run to public artifact, while collaborating with researchers and engineers for reproducible, scalable deployment.

Job description

Pluralis Research is seeking an AI engineer to advance reinforcement learning systems. You will build the RL post-training stack end-to-end, from rollout ingestion to reward computation and policy updates, and you will implement robust monitoring and versioning to keep models auditable.

You will invent algorithms for asynchronous, high-latency, partially trusted generation, including staleness tolerance and off-policy corrections, and you will design experiments that validate improvements under