Senior Machine Learning Engineer – Edge AI for Health Wearables

Das ist der Job

USound achieves this through radical miniaturization, power reduction, and increased production efficiency.

Darum lohnt es sich

What we offer The opportunity to grow along with an internationally active, innovative companyand a ground-breaking technology. Collegial environment in a dynamic and dedicated team. Benefits: We offer a range of perks including lunch subsidies, language courses, well-being and summer childcare subsidies, future provision,and many more.

Senior Machine Learning Engineer – Edge AI for Health Wearables Graz , Austria USound is disrupting the audio industry, setting new standards in audio experience, and maximizing the degrees of freedom forwearables and hearables.

USound isleaders in acoustic solutions based on MEMS speakers byenabling our customers to bring new revolutionary audio products to the market. USound isadvancing in-ear wearables into intelligent health companions.

We’re seeking a SeniorMachine Learning Engineer to lead the development of real-time AI modelsfor acoustic biosignal analysis , from conception to secure deployment onedge devices. Responsibilities Design and lead development ofadvanced ML models (CNN, LSTM, Transformers) for extracting HR, HRV, PulseWave Velocity, and Cardiac Output.

Ensure robustness of modelsagainst noise, motion, and device variability. Drive mobile & edgedeployment efforts (TensorFlow Lite, quantization, OTA updates). Mentor less-experiencedengineers and work closely with colleagues on firmware, SDK, and backenddevelopment. Contribute to security andcompliance strategies (GDPR/HIPAA).

Experience and Qualifications Master’s or PhD in ComputerScience, AI, Electrical/Biomedical Engineering, or Embedded Systems. Strong background in deeplearning, time-series analysis, and model deployment. 6+ years in ML/AI with strongfocus on time-series / biosignals. Proven expertise in deeplearning and signal processing.

Hands-on experience deployingML models on mobile or embedded systems. Strong knowledge of acousticbiosignals (APG, OAE). Comfortable working in a small,cross-functional environment. Nice-to-have Experience with federatedlearning frameworks (TensorFlow Federated, Flower). Prior work on medical-grade orregulated devices.

Knowledge of security standardsand encryption methods (AES). Flexible working time and goal-oriented workenvironment. Range of the annual gross salary from € 60,000depending on experience, qualifications, etc.; legal minimum annual gross salary on a full-time basis: € 45,207.68.

Additional incentives including bonuses and profit-sharing opportunities. At our location you will find free parking slots, a canteen and a snack buffet. #J-18808-Ljbffr