Senior Agentic Reinforcement Learning (RL) Engineer

UMELIFE (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

UMELIFE (SINGAPORE) PTE. LTD. is seeking a Senior Agentic Reinforcement Learning (RL) lead capable of directing development of autonomous AI agents for long-horizon tasks in virtual environments. You will oversee end-to-end RL post-training initiatives, shaping agent behavior and reliability.

You will guide a small team, push the boundaries of agent autonomy, and collaborate with research and evaluation teams to translate cutting-edge RL research into robust, real-world capabilities.

Qualifications

• Master’s degree or higher with at least 3 years’ hands-on RL experience.
• Experience leading a small team on end-to-end LLM post-training projects.
• Experience with RL algorithms like GRPO/PPO and LLM training frameworks veRL/OpenRLHF.

Responsibilities

• Conduct RL training experiments for long-horizon tasks, including hyperparameter tuning 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.

Skills

Agentic RL

Leadership

Experimentation

Education

Master's degree or above

Tools

GRPO

PPO

veRL

OpenRLHF

Job description

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.

JobResponsibilities

• 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.