ML Engineer: Acoustic Biosignals & Edge AI

USound GmbH; Österreich; Vor Ort; EUR 37.000 - 45.000; Vollzeit

Benefits dieser Stelle

Lunch subsidies

Language courses

Well-being subsidies

Summer childcare subsidies

Future provision

Free parking slots

Canteen

Snack buffet

Zusammenfassung

USound GmbH in Graz, Austria is seeking an early-career Machine Learning Engineer to develop AI models for acoustic biosignals and enable real-time deployment on wearables. You will work with CNNs/LSTMs, apply signal processing techniques, and collaborate with firmware teams to make models robust to motion artifacts and noise, bringing AI into production.

A bachelor’s or master’s in CS/engineering is expected, along with 2–3 years of ML/AI experience, TensorFlow or PyTorch knowledge, and a

Qualifikationen

• Bachelor’s or Master’s degree in Computer Science, Electrical/Biomedical Engineering, or Data Science.
• Coursework or projects in Machine Learning, Signal Processing, or Biosignal Analysis are a plus.
• 2–3 years of experience in ML/AI, ideally with time-series or biosignal data.
• Solid understanding of TensorFlow or PyTorch.
• Familiarity with signal processing techniques (e.g., filtering, peak detection).
• Basic experience deploying AI models on embedded or edge devices.
• Strong collaborative mindset and eagerness to learn and grow.

Aufgaben

• Contribute to deep learning models (CNN, LSTM) for acoustic biosignals (e.g., APG).
• Implement algorithms for HR,HRV, and vascular health indicators.
• Assist in making models robustto motion artifacts and noise.
• Collaborate with senior engineers and firmware teams to bring AI into production.

Kenntnisse

Machine Learning

Time-series data

Biosignal analysis

TensorFlow

PyTorch

Signal processing

Edge deployment

Team collaboration

Ausbildung

Bachelor’s or Master’s degree in Computer Science, Electrical/Biomedical Engineering, or Data Science

Jobbeschreibung

USound GmbH in Graz, Austria is seeking an early-career Machine Learning Engineer to develop AI models for acoustic biosignals and enable real-time deployment on wearables. You will work with CNNs/LSTMs, apply signal processing techniques, and collaborate with firmware teams to make models robust to motion artifacts and noise, bringing AI into production.

A bachelor’s or master’s in CS/engineering is expected, along with 2–3 years of ML/AI experience, TensorFlow or PyTorch knowledge, and a