Senior Machine Learning Researcher - Clinical AI & Voice...

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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 acquisition and endpoint definition

• Ensure validation strategies meet clinical-grade and regulatory expectations (MDR / FDA)

ML Operations & Productization:

• Translate research outcomes into deployable ML prototypes suitable for clinical evaluation

• Build reusable, modular components (feature pipelines, model architectures) to support scalable ML workflows

• Work closely with Product and Engineering teams to ensure models meet regulatory, security, and observability requirements

Leadership & Growth Opportunities:

• Influence the scientific direction of voice analytics and clinical ML research

• Define experimentation standards and validation criteria across the R&D team

• Mentor junior researchers, students, and interns, fostering a culture of rigor and learning

• Support hiring and growth of the research team

• Present research outcomes internally and externally through publications and conferences

What We’re Looking For

Required:

• PhD in Machine Learning, Computer Science, Biomedical Engineering, Signal Processing, or a related field

• 4+ years’ experience in ML research or data science (healthcare or regulated environments strongly preferred)

• Strong end-to-end experimentation experience: data preprocessing, feature engineering, model training, evaluation

• Proven track record of rigorous, reproducible research and translating findings into prototypes or publications

• Comfortable collaborating with clinicians and presenting complex concepts clearly

Technical Stack:

• Python, PyTorch and/or TensorFlow, scikit-learn

• Experiment tracking tools (e.g. Weights & Biases)

• Audio and signal processing tools (e.g. librosa, OpenSmile or similar)

• Git-based workflows, Docker, and cloud platforms (GCP or similar)

Strong Plus:

• Experience with clinical workflows and regulated AI systems (MDR / FDA)

• Publications in ML for health, speech analytics, or biosignal processing

• Experience building voice analytics or digital health ML solutions

• Background or strong interest in cardiovascular health or heart failure