Deep Learning Scientist – Foundation Models
Pubblicato il 16-08-2026 - Translated in Roma
About Translated
Translated is on a mission to allow everyone to understand and be understood, in their own language. We are a technology-powered professional translation provider. We partner with over 200,000 professional translators worldwide, in 200 languages. Our 310,000 clients range from private individuals who need their CV translated to some of the world's largest companies, such as Uber and Airbnb.
About Translated
Translated is on a mission to allow everyone to understand and be understood, in their own language. We are a technology-powered professional translation provider. We partner with over 200,000 professional translators worldwide, in 200 languages. Our 310,000 clients range from private individuals who need their CV translated to some of the world's largest companies, such as Uber and Airbnb.
Our progress is largely powered by our ability to leverage scientific progress and realize the best synergy between humans and machines. We invest heavily in R&D;, including Large Language Models, Machine Translation, expressive speech synthesis, and privacy-preserving training.
We operate as a science-driven company, where scientific innovations quickly make their way into production and have a measurable impact on our products and operations.
We are looking for a Deep Learning Scientist to join our Foundation Models team, working on the development of large-scale textual language models from scratch.
The ideal candidate has a strong interest in Large Language Models, large-scale deep learning, and experimental research, and is excited about pushing the state of the art in multilingual language modeling.
The Project: Foundation Models
Translated is building its own generation of multilingual Foundation Models, with the ambition of advancing the state of the art in open Large Language Models, particularly in multilingual capabilities.
Our work covers the complete LLM pre-training lifecycle: from data selection and curation to model architecture, scaling experim