ML & Data Engineer - Create Audio Augmentation Models

Embodied AI; Zürich; Hybrid; CHF 20.000 - 36.000; Teilzeit

Benefits dieser Stelle

Paid Master thesis or internship

One problem that is genuinely yours

Your work goes into the training stack

Flexible hybrid setup

Zusammenfassung

Aurigin.ai in Zurich is building an augmentator to make recordings sound as if captured in different environments. You will design a generative model that takes source audio and a target acoustic scenario and outputs the transformed speech.

The role blends research and implementation in Python with PyTorch, data pipelines, and Acoustic Atlas contributions. It offers a paid Master thesis or internship and a flexible hybrid setup with two days per week in the Zurich office.

Qualifikationen

• Pursuing or completed MSc in CS, EE, ML, or related field.
• Strong Python and hands-on ML, ideally PyTorch.
• Interest in audio, speech, or signal processing.
• Fluent in English; available for around 6 months; able to be in the Zurich office at least two days a week.

Aufgaben

• Build the augmentator: a generative model that takes source audio plus a target acoustic scenario and returns that voice as it would have sounded in that setup.
• Make the scenario space open-ended: represent acoustic conditions so the model can produce combinations it has never seen.
• Improve how Acoustic Atlas collects: the capture flow, the metadata, and the quality control that decides whether a contribution is usable for training.
• Get data in at volume, by crowdsourcing, paid recording sessions, open datasets, or a public competition.
• Prove it out: measure whether detectors trained on generated audio hold up better on replay and real calls; report results clearly.

Kenntnisse

Python

PyTorch

Data pipelines

English communication

Ausbildung

MSc in CS/EE/ML

Tools

Python

PyTorch

Jobbeschreibung

Aurigin.ai in Zurich is building an augmentator to make recordings sound as if captured in different environments. You will design a generative model that takes source audio and a target acoustic scenario and outputs the transformed speech.

The role blends research and implementation in Python with PyTorch, data pipelines, and Acoustic Atlas contributions. It offers a paid Master thesis or internship and a flexible hybrid setup with two days per week in the Zurich office.