At Audatic, we build systems to render speech from any sound scenario clear and free of noise using state-of-the-art deep learning technology. Our mission is to empower millions of people with hearing loss to enjoy interactions in demanding social settings like bars or restaurants.
As a part of the Sonova Group, our team developed the speech enhancement neural network for the Phonak Infinio Sphere - the world’s first hearing aid to run a deep neural network (DNN) for real-time speech enhancement on a custom-built, low-power, embedded neural accelerator. Since then, numerous clinical studies and extensive market feedback have confirmed the significant, life-changing benefits of this breakthrough technology.
As a small, supportive team within a global leader, we pair startup agility with Sonova’s scale, audiological expertise, and world-class resources. Everyone here has the chance to shape how we work, contribute their ideas and have a direct impact on the products we deliver - creating technology that can transform lives.
Please note that the role outlined below is an on-site opportunity with a hybrid setup requiring at least 60% office attendance. Fully remote work is not possible. You should ideally be located in Berlin or close enough for an easy commute, or be open to relocating.
Deep Learning Engineers form the core of our team at Audatic. If you are excited to develop novel algorithms for customizable sound environments, we have a fantastic job opportunity for you!
Your responsibilities will include:
Audatic is an equal opportunity employer. We team up. We grow talent. We collaborate with people of diverse backgrounds to win with the best team in the market place. We guarantee every person equal treatment in regard to employment and opportunity for employment, regardless of a candidate’s ethnic or national origin, religion, sexual orientation or marital status, gender, genetic identity, age, disability or any other legally protected status.
#J-18808-LjbffrVeröffentlichungsdatum:
18 Mai 2026Standort:
BerlinTyp:
VollzeitArbeitsmodell:
Vor OrtKategorie:
Erfahrung:
2+ yearsArbeitsverhältnis:
Angestellt
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