Senior Machine Learning Researcher – Clinical AI & Voice Analytics

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Senior Machine Learning Researcher – Clinical AI & Voice Analytics

Location: Berlin

Type of role: Permanent

Sector: Digital Health / Medical Technology (Clinical AI & Machine Learning)

Working environment: Office-based with flexibility

Breakdown of hybrid arrangement: Hybrid (minimum 3 days in the office) or remote (less preferred)

Working days and hours: Monday to Friday, full time

Language requirements: Fluent in spoken and written English

Benefits:

• A competitive compensation package with the possibility of receiving company shares

• Macbook

• Iphone

• Access to the Urban Sports Club so you can stay fit.

• The unique opportunity to positively influence the lives of millions of people.

• A dynamic start-up with an ambitious, interdisciplinary team.

 We are supporting an innovative digital health company that is redefining how heart failure is monitored and managed using advanced machine learning and voice analytics. Their technology enables earlier detection of clinical deterioration than traditional methods, improving outcomes for patients at scale.

They are now looking for a Senior Machine Learning Researcher to join their growing R&D team and play a key role in developing clinical-grade AI models, translating cutting-edge research into deployable solutions used in real-world healthcare settings.

This is not a pure research-in-isolation role. You will sit at the intersection of machine learning research, clinical science, and product development, working closely with medical experts and product teams in a fast-moving startup environment.

What You’ll Do

Research & Experimental Development:

• Design, execute, and interpret machine learning experiments on voice-based biomarkers for early detection of heart failure decompensation

• Research and prototype ML approaches aligned with clinical objectives, delivering proof-of-concept models

• Explore signal processing and voice analytics techniques to model physiological relationships between vocal features and cardiovascular states

• Design experiments grounded in clinical hypotheses, with strong emphasis on validation and reproducibility

• Maintain clean, reproducible research workflows (versioned datasets, tracked experiments, model registries)

 Clinical Alignment & Translational Impact:

• Develop a strong understanding of heart failure physiology and real-world clinical workflows

• Collaborate closely with cardiologists and clinical researchers to ensure models reflect clinical reality

• Align research methods with ongoing and future clinical studies, including data acq