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.