Senior Agentic Reinforcement Learning (RL) Engineer

We are looking for a Senior Agentic Reinforcement Learning (RL) to lead the development of next-generation AI agents capable of autonomously executing complex, long-horizon tasks across virtual environments.

This is an exciting opportunity to drive cutting-edge LLM post-training and Agent RL, working on advanced techniques such as GRPO/PPO, trajectory-based training, and sandbox environment integration. You will lead a team to push the boundaries of agent autonomy, reliability, and task completion, turning advanced AI research into real-world capabilities.

Job Responsibilities

Conduct reinforcement learning (RL) training experiments for long-horizon tasks, including hyperparameter tuning, performance analysis, and iterative optimization.

Manage agent trajectory data feedback and integration into the training pipeline, as well as coordinate and optimize sandbox environment integration.

Work closely with the evaluation team to validate model capabilities, analyze evaluation results, and continuously improve the model's success rate on long-horizon tasks.

Requirements

Master's degree or above, with at least 3 years of hands-on experience in Reinforcement Learning (RL), Reinforcement Learning from Human Feedback (RLHF), or Reinforcement Learning with Verifiable Rewards (RLVR). Experience in Agent-related projects is preferred.

Familiarity with mainstream RL algorithms such as GRPO and PPO, as well as LLM training frameworks such as veRL and OpenRLHF.

Experience leading a small team, with a proven track record of delivering end-to-end LLM post-training projects.

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