Senior Machine Learning Researcher (Clinical AI & Voice Analytics)

At Noah Labs , we are pioneering a new era of digital cardiology . 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 .

Tasks

1. Core Responsibilities

A. Research and Experimental Development

• Plan, design, execute, and interpret ML experiments on voice-based biomarkers for heart failure decompensation prediction.
• 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.
• Maintain a clean, reproducible development environment (versioned datasets, tracked runs, model registries).

B. Clinical Insight and Translational Alignment

• Develop deep understanding of heart failure decompensation, including physiological mechanisms and clinical workflows for monitoring and management.
• 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).
• Ensure analytical pipelines, validation strategies, and results meet clinical-grade standards and comply with MDR/FDA regulatory expectations.

C. ML Operations and Clinical Productization

• 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.
• Collaborate closely with the Product team to ensure deployment meets regulatory, security, and observability requirements for clinical environments.

2. Leadership and Growth Opportunities

A. Scientific Direction and Strategic Influence

• 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.
• Promote scientific rigor, curiosity, and collaboration across the R&D team, ensuring technical excellence and clinical relevance.
• 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.

B. Mentorship and Team Development

• Lead and mentor team members by defining clear goals, conducting regular reviews, and fostering a culture of efficiency, accountability, and continuous learning.
• 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.

C. Scientific Communication and Clinical Impact

• Present research outcomes and clinical insights to internal teams, senior researchers, cardiologists, and external medtech partners.
• Represent Noah Labs within the scientific community through publications, conference presentations, and professional engagements.
• Communicate progress, challenges, and strategic recommendations to the CTO and CMedO to support company-wide decision-making.

Requirements

A. Education and Background

• PhD in Machine Learning, Computer Science, Biomedical Engineering, Signal Processing, or a related discipline.
• Senior-level track: 4+ years of experience in ML research or data science, ideally with exposure to healthcare or regulated data environments.

B. 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.
• Track record of rigorous, reproducible experimentation and translating findings into actionable prototypes or publications.
• Comfortable presenting to clinical partners and at scientific or startup events.

C. Technical Expertise

• Core ML stack: Python, PyTorch/TensorFlow, scikit-learn, Weights & Biases for experiment tracking.
• Signal and audio analytics: Familiarity with librosa, OpenSmile, or equivalent frameworks.
• Infrastructure and reproducibility: Experience with Git-based workflows, continuous integration for research code, Docker, and GCP or other cloud platforms.

D. Behavior and Communication

• Clear, structured communicator with strong writing skills and consistent documentation of decisions, assumptions, and results.
• Values autonomy with accountability; thrives in in-person collaboration and rapid, iterative experimentation.

E. 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.

F. Nice-to-Haves

• 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.

How You’ll Work with Us

• Reporting line: Reports to the CTO and collaborates closely with the CMedO, Product, Development, and external clinical and scientific advisors.
• Position: Full-time, on-site role emphasizing fast iteration, hands-on experimentation, and close cross-functional collaboration.

Benefits