Engineering Ilp Day - Sapienza
Pubblicato il 28-08-2026 - Translated in Roma
**About Translated** Translated is on a mission to make content in all languages accessible to everyone. We are a technology-powered professional translation provider. We partner with over 180 000 professional translators, i.e. about a third of the translator workforce alive today. Our 140 000 clients range from the private person who needs their CV translated to the very big, like Google and Airbnb. We have invested over 6 000 000 € in R&D; in the past years. Our EU H2020 project Matecat produced the translation GUI which allows our translators to edit sentence translations without having to worry about document layout and formatting at all. With ModernMT, another H2020 project which won an accolade from the EU as one of the 3 best projects of the call, we developed our neural adaptive machine translation technology, which is available open-source on Github. Our research papers and more information about our research activity can be found on our website. We use AI techniques in several places in our platform. Of course, machine translation is our core enabling technology. Beyond that, we use ML to deal with our massive pool of translators: for instance, assigning an incoming translation job to the best qualified translator available is an algorithmic process which uses matches against the translator's job history, as well as past performance. We also need to rank translators for whom we have no performance data: our algorithms produce estimates based on their CVs. We would like to make progress in a number of different areas which require AI, ML and data science skills: - _ marketing_: can we predict predict what customer retention activities work best?
- _ NLP_:
with our millions of translation edits at sentence-level, can we create a linguistically-informed edit distance?
- _ low-resource machine translation_: out of the 140 languages we process, most have few NLP resources, and translation models have a hard job. How can we improve this, for instance with transfer learning or language similarity?
- _ probabilistic modelling_: Tensorflow 2.0 supports Edward; can we probabilistically model in our linguistic QA processes, i.e. translation scoring, quality score prediction etc.? Do you think you can help us with this? If yes, write to us, and we'll build a project for your internship. **Headquarter (Rome - Italy)** Translated is hosted at Pi Campus, a working environment immersed in nature where 5 luxury villas have been converted into functional offices to foster talent growth. Pi Campus is also a venture firm created by Translated to reinvest part of its profits into promising AI startups. **Benefits and Perks**
- Gym
- Swimming Pool
- Kickboxing, water aerobics, fitness and Pilates classes
- Table Tennis and Football table
- Kitchen and snacks **Diversity statement** At Translated, we proudly embrace and celebrate each individual's unique qualities to our team, regardless of race, sexual orientation, gender identity, or any other differences. We recognize that these diverse perspectives empower us to overcome challenges, foster innovation, and drive excellence. As an inclusive and equal-opportunity employer, we are committed to cultivating an environment where everyone feels welcome, valued, and supported to achieve their full potential. Privacy Policy ypqKsjTNBu