Senior Agentic Reinforcement Learning (RL) Engineer

UMELIFE (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 150,000 - 230,000; Full time

Job summary

UMELIFE (SINGAPORE) PTE. LTD. seeks a Senior Agentic Reinforcement Learning (RL) lead to drive development of autonomous AI agents for complex, long-horizon tasks in virtual environments. You will advance agent autonomy, reliability, and task completion through cutting-edge RL techniques and LLM post-training work.

The role focuses on agent RL, GRPO/PPO, trajectory-based training, and sandbox integration, guiding a small team to translate research into real-world capabilities.

Qualifications

• Master's degree or above in a relevant field required.
• 3+ years hands-on experience in Reinforcement Learning (RL), RLHF, or RLVR.
• Experience leading agent-related projects or small teams is preferred.

Responsibilities

• Conduct RL training experiments for long-horizon tasks, tuning hyperparameters and analyzing performance.
• Manage agent trajectory data feedback and sandbox environment integration into the pipeline.
• Collaborate with evaluation teams to validate capabilities and improve success rates on long-horizon tasks.

Skills

Reinforcement Learning

RLHF

RLVR

Agent projects

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.