RL环境软件工程师
RL Environment Software Engineer
Location: 远程（美国）
Work Model: 远程
Industry: 应用AI / AI研究数据
Compensation: 基础薪资18万美元-22万美元，外加约40万美元+不封顶的利润分成
About the Company
我们的合作伙伴是一家快速发展的应用AI研究实验室，他们为全球领先的AI实验室构建高质量的强化学习环境和代理。在不到两年的时间里，他们的年收入已达到数亿美元，并且在几个月内团队规模扩大了好几倍，获得了顶级风险投资的支持。质量是他们的核心差异化优势，他们正在迅速扩展到新的领域。
The Opportunity
作为RL环境软件工程师，你将处于研究工程和传统软件工程的交汇点，构建模拟现实工作流程的环境以及自动化这些流程的代理。这是一项具有前瞻性的任务，你将帮助研究和预测前沿领域需要的高质量环境，然后从零开始构建它们。
你将加入一个全新的RL团队，该团队由优秀的人才组成，随着功能扩展到行业模块，你将有明确的路径与之共同成长。
Responsibilities
- 设计并构建端到端的高质量RL环境，模拟真实的工作环境。
- 为这些环境中的任务开发代理，并不断迭代直到它们高效且可投入生产。
- 与研究团队合作，确定要构建哪些环境以及原因，提前应对未来需求，而不仅仅是满足当前需求。
- 负责使环境可靠和可扩展的后端和基础设施层。
- 在RL功能增长的过程中，帮助建立工程标准。
Requirements
- 具备强大机器学习背景的工程师，能够大量编写代码并从零构建系统，对强化学习有深刻直觉。
- 熟练掌握现代技术栈，后端使用Node.js和Python，前端使用React/TypeScript，具备扎实的Kubernetes和Docker技能。
- 能够在快节奏的初创环境中工作，具备高度的责任感和长时间工作能力。
- 在之前公司有显著任职时间和影响力记录。
- 具备强化学习经验或RL研究背景是一个很大的加分项，但不是必须的。
- 计算机科学或相关技术领域的学士学位，或同等的实际经验。
Originally posted on Himalayas
查看英文原文
Location: Remote (United States)
Work Model: Remote
Industry: Applied AI / AI research data
Compensation: $180K-$220K base, ~$400K+ OTE (uncapped profit share)
About the Company
Our partner is a fast-growing applied AI research lab that builds high-quality reinforcement-learning environments and agents sold to the world's leading AI labs. In under two years they have scaled to a nine-figure revenue run rate and grown their team severalfold in a matter of months, backed by leading venture investors. Quality is their core differentiator, and they are rapidly expanding into new domains.
The Opportunity
As an RL Environment Software Engineer, you will sit at the intersection of research engineering and traditional software engineering, building the environments that simulate real-world workflows and the agents that automate them. This is forward-looking work, you will help research and predict what high-quality environments the frontier will need next, then build them from the ground up.
You will join a brand-new RL team being assembled with exceptional talent, with a clear path to grow alongside it as the function scales into industry pods.
Responsibilities
- Design and build high-quality RL environments that simulate real working environments end to end.
- Develop agents for the tasks within those environments and iterate until they are efficient and production-ready.
- Partner with the research team to scope which environments to build and why, staying ahead of future demand rather than only meeting present needs.
- Own the backend and infrastructure layers that make environments reliable and scalable.
- Help set engineering standards for a zero-to-one team as the RL function grows.
Requirements
- Strong machine-learning engineers who code heavily and build systems from scratch, with strong intuition for reinforcement learning.
- Proficiency across a modern stack, Node.js and Python on the backend and React/TypeScript on the frontend, with strong Kubernetes and Docker skills.
- Comfort operating in a fast-paced startup environment with high ownership and long hours.
- A track record of meaningful tenure and impact at previous companies.
- Reinforcement-learning experience or an RL research background is a strong plus, though not required.
- Bachelor's degree in computer science or a related technical field, or equivalent practical experience.
Originally posted on Himalayas