Our flagship technology, Noah Labs Vox, applies advanced machine learning to the human voice to detect early signs of heart failure worsening —earlier than any existing method—transforming how clinicians monitor and manage their patients.
Building on a strong foundation of international clinical trials, we are entering the next chapter: bringing this breakthrough technology to market and expanding the clinical evidence base to establish Vox as the new standard of care.
Together with our deployed remote monitoring platform, Noah Labs Ark —already trusted by more than 200 cardiologists and 1,000+ patients across Europe and the United States—Noah Labs Vox will scale our remote monitoring ecosystem and shape the future of cardiovascular care.
Research and Experimental Development
Research and prototype machine learning techniques aligned with clinical objectives, delivering proofs of concept for promising methods.
Explore voice analytics and signal processing approaches to uncover and model physiological relationships between vocal features and cardiovascular states, with a focus on early detection of heart failure decompensation.
Use data-driven clinical hypotheses to design experiments with rigorous validation and reproducibility.
Collaborate closely with medical stakeholders to translate hypotheses into meaningful data experiments.
Work closely with cardiologists, clinical researchers, and study coordinators to ensure model design, data acquisition, and interpretation align with real-world clinical practice.
Align research methods and milestones with ongoing and planned clinical studies (data acquisition design, endpoint definition, monitoring processes).
Translate research findings into actionable insights and deployable ML prototypes suitable for clinical workflows and real-world evaluation.
Design and maintain reusable, modular components (feature stores, preprocessing pipelines, model architectures) to support scalable, clinical-grade ML workflows.
Shape the research direction for voice analytics and machine learning at Noah Labs by defining methodologies, establishing experimentation standards, and ensuring alignment with company objectives.
Coordinate multidisciplinary projects, define milestones, and manage interfaces with Product, Clinical, and Engineering teams.
Establish rigorous validation criteria to ensure the reliability and clinical value of research outcomes.
Supervise Master’s theses and student interns, providing structured mentorship with clear deliverables and ongoing feedback.
Support hiring efforts by identifying, interviewing, and onboarding top R&D talent.
Present research outcomes and clinical insights to internal teams, senior researchers, cardiologists, and external medtech partners.
Communicate progress, challenges, and strategic recommendations to the CTO and CMedO to support company-wide decision-making.
PhD in Machine Learning, Computer Science, Biomedical Engineering, Signal Processing, or a related discipline.
4+ years of experience in ML research or data science, ideally with exposure to healthcare or regulated data environments.
Experience and Research Practice
Proven end-to-end experimentation experience: data preprocessing, feature engineering, model training, evaluation, and error analysis.
Demonstrated ability to supervise students or junior researchers and lead small-scale research projects.
Comfortable presenting to clinical partners and at scientific or startup events.
Python, PyTorch/TensorFlow, scikit-learn, Weights & Biases for experiment tracking.
Signal and audio analytics: Experience with Git-based workflows, continuous integration for research code, Docker, and GCP or other cloud platforms.
Domain Knowledge and Compliance Awareness
Familiarity with clinical workflows, medical evidence standards, and exposure to MDR/FDA expectations for AI/ML systems is a strong plus.
Comfortable collaborating with clinicians and translating research outcomes into study protocols or product requirements.
Publications or conference presentations in machine learning for health, speech analytics, or biosignal processing.
Hands-on experience building voice analytics solutions in digital health settings.
Research background in cardiovascular health or heart failure decompensation, with curiosity for how AI can uncover new physiological insights.
Familiarity with medically regulated AI products and enthusiasm for translating cutting-edge research into real-world clinical practice.
Position: Full-time, on-site role emphasizing fast iteration, hands-on experimentation, and close cross-functional collaboration.
Benefits
&##A dynamic startup with a diverse team and exceptional talent from Harvard, TUM, Meta and Stanford.
&##researchers from the global cardiology community with the world’s best
medical institutions.
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