Applied AI Researcher – Foundation Models
Pubblicato il 23-08-2026 - Translated in Roma
About Translated Sia tra i primi a candidarsi: legga la panoramica completa del ruolo qui sotto, quindi invii la sua candidatura per essere preso/a in considerazione.Translated is a leading provider of AI-powered language solutions. Founded in 1999 by a linguist and a computer scientist, we are on a mission to allow everyone to understand and be understood in their own language.
We envision a world where people from different cultures can communicate seamlessly, gaining unprecedented access to knowledge, cultural exchange, and opportunity. To make this possible, we combine proprietary translation AI (Lara Translate) with advanced text, audio, and video translation technologies (Matecat, Matesub, and Matedub) and the world's largest network of vetted, native-speaking language professionals.
We welcome complex technical challenges from our customers and engineer tailored solutions that often become part of our core products, whether integrated into our enterprise localization platform, the tools designed to support translators, or Lara Translate,
our online AI translator for teams and individuals.
Everything we build reflects a simple principle: technology should amplify human potential, not replace it.
We believe in humans.
We are looking for an Applied AI Researcher 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 ModelsTranslated 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 experiments, large‐scale distributed