Reinforcement Learning Algorithm Engineer (Agent Reinforcement Learning Engineer)

About the Role

We are looking for Agent Reinforcement Learning Engineers to join our Agent Core team.

You will help build learning-capable AI agents that:

•Interact continuously with real-world business environments

•Learn decision policies for pricing, inventory, and operations

•Perform long-horizon reasoning and planning

•Optimize behavior through feedback and preference alignment

•Improve themselves over time in production

This role focuses on applying reinforcement learning together with large language models and agent architectures in real industrial systems — not simulated toy environments.

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Focus

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

•Apply reinforcement learning to real scenarios such as pricing optimization, inventory allocation, and fulfillment scheduling

•Build long-horizon planning and multi-step reasoning pipelines for agents

•Implement preference learning and feedback optimization (RLHF / RLAIF / online learning)

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

•Build closed learning loops: Sense → Decide → Act → Feedback → Improve

•Develop automated training, evaluation, and deployment workflows

•Improve observability and stability of large-scale RL jobs

•Refactor agent, data, and training frameworks for production readiness

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Ideal Experience

•Background in reinforcement learning, agents, or decision systems

•Strong Python + PyTorch

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

•Systems thinking mindset

Nice to have:

•Multi-agent experience

•Operations research / game theory

•Supply chain, pricing, or resource optimization exposure

•LLM agent frameworks (LangGraph, AutoGen, CrewAI)

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Typical Problems You’ll Work On

•A pricing strategy behaves differently across regions — how should the agent adapt via reinforcement learning?

•Inventory and fulfillment objectives conflict — how can agents learn trade-offs between profit, cost, and service level?

•Business data is noisy and delayed — how do we design robust reward functions?

•Enterprise preferences shift — how do we quickly realign agent behavior?

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Tech Stack

Python / PyTorch

Distributed RL

Agent frameworks

TypeScript / React (internal tools)