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. seeks a Senior Agentic Reinforcement Learning lead to drive next‑gen AI agents capable of autonomously executing long-horizon tasks across virtual environments. You will lead a team, advance LLM post-training and Agent RL, and push boundaries in agent reliability and task completion.

The role involves developing training pipelines with GRPO/PPO, trajectory-based training, and sandbox integration to translate research into real-world capabilities.

Qualifications

• Master's degree or higher with hands-on RL/RLHF/RLVR experience (minimum 3 years).
• Experience leading small teams on end-to-end LLM post-training projects preferred.
• Familiarity with agent-focused projects and long-horizon task goals.

Responsibilities

• Conduct RL training experiments for long-horizon tasks, including hyperparameter tuning and analysis.
• Manage agent trajectory data and sandbox environment integration into the training pipeline.
• Collaborate with evaluation team to improve model success rate 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.

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.