Senior Agentic Reinforcement Learning (RL) Engineer

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

Job summary

KUAILU SOFTWARE (SINGAPORE) PTE. LTD. seeks a Senior or Head Agentic Reinforcement Learning (RL) leader to drive next-generation AI agents capable of autonomously completing long-horizon tasks in virtual environments.

You will lead a team focused on agent autonomy, reliability, and real-world applicability, advancing RL, RLHF, RLVR, and LLM post-training techniques in sandboxed and evaluated settings.

Qualifications

• Master's degree or higher with 3+ years RL experience.
• Experience with RL algorithms such as GRPO and PPO.
• Experience leading a small team in end-to-end LLM post-training projects.

Responsibilities

• Lead RL experiments for long-horizon tasks and hyperparameter tuning.
• Coordinate sandbox environment integration and trajectory data feedback.
• Collaborate with evaluation team to improve model success rate.

Skills

RL

RLHF

RLVR

Team leadership

LLM training

Education

Master's degree

Tools

veRL

OpenRLHF

GRPO

PPO

Job description

We are looking for a Senior or Head 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.