研究工程师，数据基础
Research Engineer, Data Foundations
我们正在通过融合艺术与科学，构建人工智能来模拟世界。
我们认为，世界模型是人工智能进步的前沿。仅靠语言模型无法解决世界上最难的问题——机器人技术、疾病、科学发现。真正的进步需要能够体验世界并从错误中学习的模型，就像人类一样。而这种试错过程在模拟环境中进行时，可以被大幅加速，而不是在现实世界中。
世界模型为通用模拟提供了最清晰的路径，改变了故事的讲述方式、科学进步的方式以及人类下个前沿领域的探索方式。
我们的团队由富有创造力、思想开放、有爱心且有抱负的人组成，他们决心改变世界。我们渴望不断创造不可能的事物，而我们的能力依赖于打造一支卓越的团队。如果你也有同样的动力，我们很期待收到你的消息。
### 关于该职位
构建通用世界模型——能够跨任务、模态和领域理解并模拟现实的系统——需要的数据集与现实世界一样丰富和多样。我们正在寻找研究工程师来负责我们模型背后的数据：它们学了什么、学得如何，以及能解锁哪些新功能。你将设计数据集，运行建模实验，并构建大规模生成和整理数据的基础设施——直接影响我们的模型能做什么，应用范围从创意工具到机器人技术。
### 你会做什么
- 设计多模态、多任务的数据集，教会世界模型新的能力——决定收集、生成或整理哪些数据，并衡量其对模型行为的影响
- 运行受控的训练实验，了解数据组成如何影响模型在不同任务和领域中的表现
- 构建和运营大规模的合成数据生成、过滤和质量控制流程
- 定义评估和基准测试，以衡量我们的模型是否真的在关键方面有所提升
- 与产品和创意团队合作，将目标行为和能力转化为具体的数据策略
### 你需要具备
- 4年以上机器学习经验，有数据驱动方法经验者优先
- 有多模态大型数据集和生成模型（视频、图像或多模态）的经验
- 对数据组成和质量如何影响模型有深刻直觉
查看英文原文
We are building AI to simulate the world through merging art and science.
We believe that world models are at the frontier of progress in artificial intelligence. Language models alone won’t solve the world’s hardest problems – robotics, disease, scientific discovery. Real progress requires models that experience the world and learn from their mistakes, the same way that humans do. And this kind of trial and error can be massively accelerated when done in simulation, rather than in the real world.
World models offer the most clear path to general-purpose simulation, changing how stories are told, how scientific progress is made and how the next frontiers of humanity are reached.
Our team consists of creative, open minded, caring and ambitious people who are determined to change the world. We aspire to continuously build impossible things and our ability to do so relies on building an incredible team. If you are driven to do the same, we'd love to hear from you.
### About the role
Building general world models — systems that understand and simulate reality across tasks, modalities, and domains — demands training data that is as rich and varied as the real world itself. We’re looking for Research Engineers to own the data behind our models: what they learn from, how well they learn it, and what new capabilities that unlocks. You will design datasets, run modeling experiments, and build the infrastructure to generate and curate data at scale — directly shaping what our models can do, with applications ranging from creative tools to robotics.
### What you'll do
- Design multimodal, multitask datasets that teach world models new capabilities — deciding what data to collect, generate, or curate and measuring its effect on model behavior
- Run controlled training experiments to understand how data composition drives model performance across tasks and domains
- Build and operate large-scale pipelines for synthetic data generation, filtering, and quality control
- Define evaluations and benchmarks that measure whether our models are actually improving at the things that matter
- Partner with product and creative teams to translate target behaviors and capabilities into concrete data strategies
### What you'll need
- 4+ years of experience in machine learning, bonus points for data-centric approaches
- Experience with large multimodal datasets and generative models (video, image, or multimodal)
- Deep intuition for how data composition and quality translate to model capabilities
- Comfort working across the full research stack: data analysis, dataset creation, model training, evaluation, and back again
- Proficiency with at least one ML framework (e.g. PyTorch, JAX) and distributed compute tools (e.g. Ray, Kubernetes)
- Excitement about building AI that simulates the world
Runway strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on competitive market rates for our size, stage and industry, and salary is just one part of the overall compensation package we provide.
There are many factors that go into salary determinations, including relevant experience, skill level and qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.
Lastly, the provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range, which again, will be communicated to candidates.
#### **Working at Runway**
[**Great things come from great teams.**](https://www.youtube.com/watch?v=kwmj4ato2kw&ab_channel=Runway) **We’d love to hear from you.**
We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. So regardless of race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply.
More about Runway
- [Universal World Simulator](https://runwayml.com/world-simulator.html)
- [GWM-1](https://runwayml.com/research/introducing-runway-gwm-1)
- [Gen-4.5](https://runwayml.com/research/introducing-runway-gen-4.5)
- [General World Models](https://runwayml.com/research/introducing-general-world-models)
- [Robotics SDK](https://runwayml.com/research/introducing-runway-gwm-1#robotics-section)
- [Conversational Real-time Agents](https://runwayml.com/research/introducing-runway-gwm-1#avatars-section)
- [Runway Studios](https://runwayml.com/studios)
We're excited to be recognized as a best place to work:
[Crain's](https://www.crainsnewyork.com/awards/best-places-work-2023) | [InHerSight](https://www.inhersight.com/companies/best/city/new-york-city-ny) | [BuiltIn NYC](https://builtin.com/awards/new-york-city/2024/best-places-to-work) | [INC](https://www.inc.com/best-workplaces/2024)