Member of Technical Staff, Research Tooling & Data Platform

Remote 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

We're looking for an engineer to own Runway's internal exploratory data analysis (EDA) and evaluation platform used daily by our ML research, design, product, and creative teams. This is a high-impact role where you'll directly accelerate research velocity and enable better decision-making across the company. This platform helps researchers query large-scale datasets, run evaluations on model outputs, and analyze results - all through an intuitive interface. As the owner, you'll be responsible for the full product experience: from database query optimization and infrastructure management to building user-facing features that make complex ML workflows accessible to non-engineers.

A peek at our technical stack

Our API endpoints for real-time collaboration and media asset management is written in TypeScript, and runs in ECS containers on AWS Fargate. We leverage multiple AWS-native components, such as S3, CloudFront, Lambda, Kinesis, and SQS, as building blocks of our infrastructure. Our inference backend is written in Python (PyTorch, TorchScript), and is deployed across multiple clusters / cloud providers. We use Kubernetes for container orchestration, and k8s-native components such as Flyte, Kueue, and Kyverno efficient job orchestration. We invest in prometheus and grafana for monitoring, and Terraform to manage our infrastructure.

What you’ll do

• Own the EDA platform end-to-end: Take full ownership of architecture,...