Runway
Member of Technical Staff, ML Platform
- Jenis pekerjaan
- Penuh waktu
- Cara kerja
- Remote
- Lokasi
- Remote
- Dipublikasikan
- 8 Okt 2026
- Terakhir diperiksa
- 26 Okt 2026
Tentang posisi ini
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.
*Titles posted to our careers page are updated regularly to best advertise the role and expected seniority, but internally we have a flat titling system with two types of titles: one for individual contributors and one for people managers.
ABOUT THE ROLE
We're looking for an ML infrastructure engineer to own model evaluation at Runway, end to end. Every decision we make about a model – which checkpoint to keep training, what to ship to millions of users, which datamix and architecture shows the most promise – rests on evals. Today that work is spread across the organization. You'll turn it into one platform.
Your job will be to design and build the systems that generate samples at scale, score them with automated metrics and human annotations, track results across checkpoints and releases, and put the answers in front of researchers in minutes rather than days. You'll define what "better" means operationally – how we measure it, how confident we are, and how a result becomes a ship/no-ship decision.