Agent Reinforcement Learning Engineer

Department: AI Agent Research Center

Location: Hong Kong / Shenzhen

Experience: Graduate / Early Career

Openings: 10

About the Role:

You will help build learning-capable AI agents that interact with real-world business environments, learn decision policies for pricing/inventory, and optimize behavior through feedback. This is about RL + LLM + Multi-agent coordination in real industrial systems.

Key Focus:

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Design agent-environment interaction systems (observations, actions, rewards).

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Apply RL to pricing optimization, inventory allocation, and fulfillment scheduling.

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Build long-horizon planning and multi-step reasoning pipelines.

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Implement preference learning and feedback optimization (RLHF / RLAIF).

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Construct simulation environments and offline evaluation pipelines from real business data.

Ideal Experience:

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Background in RL, agents, or decision systems; Strong Python & PyTorch.

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Ability to abstract real-world problems into states, actions, and rewards.

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Nice to have: Multi-agent experience, Game theory, or Supply chain optimization.

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Tech Stack: Python, PyTorch, Distributed RL, Agent frameworks.

Please send English CV to : [email protected]