Lisbon, Portugal: Unbabel announces the opening of a new office in Pittsburgh, PA, USA, one of the world’s most active hubs of Machine Translation and Natural Language Processing research and applied technology.
Led by Alon Lavie, VP of Language Technologies and an adjunct professor at the Language Technologies Institute at Carnegie Mellon University (CMU), the new office location aims to leverage the long-term ties and strong relationship between Unbabel and the university.
Lavie believes that core Machine Translation technology has improved significantly in the past several years and that Unbabel’s Customer Service Solution is a fantastic use case for it. “With multinational companies boasting customer bases throughout the world, the opportunity to use Machine Translation coupled with human feedback to provide a better level of customer support is exactly what we should be doing with the latest core technology,” he added.
Unbabel has already hired two Research Scientists and a Research Engineer to join the new team. Unbabel is recruiting for the office and has two full-time vacancies in Pittsburgh, one for Senior Research Engineer and another for a Senior Research Scientist.
“What Unbabel has done so far is still very much the beginning of what it wants to do and the laboratory will help explore more ambitious future scenarios.” said Vasco Pedro, co-founder and CEO at Unbabel.
About Unbabel
Unbabel enables modern enterprises to serve customers in their native languages, with always-on, scalable translation across digital channels.
Powered by AI and refined by a global community of translators, Unbabel combines the speed and scale of machine translation with the authenticity that can come only from a native speaker.
Unbabel has raised over $31M in funding and has over 200 employees across its Lisbon headquarters and offices in San Francisco, New York, and Pittsburgh. Leading brands like Facebook, Microsoft, Booking.com, and easyJet use Unbabel to make their customers happier and their support operations vastly more efficient.
