🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
部门： AI智能体研究中心 地点： 香港, 深圳 工作经验： 实习生、应届生 招聘人数： 10人 关于岗位 我们正在招聘 AI 智能体强化学习工程师，加入 echOS Agent 核心团队。 你将直接参与构建在真实产业环境中运行的智能体系统，让 Agent： •与复杂业务环境持续交互 •学习定价、库存、调度等决策策略 •具备长程规划能力 •基于真实反馈持续自我进化 •根据企业偏好动态调整行为 这是强化学习 + 大模型 + 多智能体协同在真实商业系统中的落地，而不是模拟世界里的 benchmark。 ⸻ 工作重点（Focus） •为产业级 AI Agent 构建环境交互系统（业务状态 / 行为空间 / 奖励建模） •将强化学习引入真实场景，如渠道定价优化、库存分配、履约调度 •构建 Agent 长程规划与复杂任务拆解能力 •实现偏好学习与反馈优化（企业目标、风险约束、利润权衡） •设计仿真环境与离线评估体系，用于训练与回放真实业务策略 •构建 Agent 学习闭环：感知 → 决策 → 执行 → 反馈 → 进化 •搭建自动化训练、评估与部署流水线 •提升大规模 RL 任务的稳定性与可观测性 •重构 Agent / 数据 / 训练框架，使研究成果快速进入生产系统 ⸻ 理想经验 •有强化学习 / Agent / 决策系统经验 •能将现实问题抽象为状态、动作、奖励 •熟悉 PyTorch / Python •有系统工程意识 •对真实商业系统有好奇心 加分项： •多智能体系统 •运筹优化 / 博弈 •供应链 / 定价 / 资源调度经验 •LLM Agent 框架 ⸻ 你将面对的典型问题 •一个渠道价格策略在不同区域表现完全不同，Agent 如何通过强化学习自动适配？ •库存与履约冲突时，如何让 Agent 学会权衡利润、时效与风险？ •新增一个业务模块，如何快速构建可学习环境？ •真实业务数据噪声极大，如何设计鲁棒 reward？ •企业偏好变化时，如何让 Agent 快速重新对齐目标？ ⸻ 技术栈 Python / PyTorch 分布式 RL Agent 框架 Typescript / React（内部工具） Department: AI Agent Research Center Location: Hong Kong, Shen Zhen Work Experience: Graduate / Early Career Number of openings: 10 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. ⸻ 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 ⸻ 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) ⸻ 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? ⸻ Tech Stack Python / PyTorch Distributed RL Agent frameworks TypeScript / React (internal tools
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.