As a Biomedical Signal Processing Engineer, you will transform raw physiological signals collected from our clinical studies into robust, interpretable, and clinically meaningful features.

You’ll collaborate closely with data scientists, clinicians, and software engineers to design signal-processing pipelines, validate features, and ensure the resulting models are explainable, scalable, and clinically credible.

Let’s Talk About Responsibilities

Key responsibilities include:

• Designing and implementing signal-processing and feature-engineering pipelines for time-series and multi-modal data.

• Developing algorithms that transform physiological signals into features with high reproducibility and interpretability.

• Collaborating with the data science team to support training and validation of models.

• Performing exploratory data analysis and establishing quality control metrics for signal integrity and feature stability.

• Building and maintaining well-documented, modular codebases (Python, MATLAB, etc.) for deployment in research and production environments.

• Supporting the clinical research and medical affairs teams in defining study data requirements and ensuring consistent signal labeling and processing.

• Contributing to explainability and traceability frameworks that help translate AI-driven outputs into clinician-friendly insights.

• Working cross-functionally with software engineers, product designers, and researchers to align technical outputs with product and clinical goals.

Let’s Talk About Qualifications and Experience

Required:

• Bachelor’s or Master’s degree in Biomedical Engineering, Electrical Engineering, Computer Science, or a related discipline.

• Demonstrated experience in biomedical signal processing, feature engineering, or algorithm development for physiological data (e.g., ECG, PPG, EEG, oximetry).

• Proficiency in Python (NumPy, Pandas, SciPy, scikit-learn, SHAP, Matplotlib) and/or MATLAB.

• Strong understanding of time-series analysis, frequency-domain methods, and signal filtering techniques.

• Ability to translate physiological knowledge into engineered features that improve model performance and interpretability.

• Experience with machine learning workflows (training, validation, cross-validation, and feature selection).

• Excellent documentation, collaboration, and communication skills.

• Passion for improving clinical outcomes through applied engineering and data science.

Preferred:

• PhD in Biomedical Engineering, Applied Mathematics, or related field.

• Prior work with sleep, cardiopulmonary, or autonomic physiology data.

• Experience working with clinical research data.

• Knowledge of AI explainability techniques and model transparency tools (e.g., SHAP, LIME).

What Success Looks Like

• Robust and reproducible signal-processing pipelines ready for clinical deployment.

• Clear feature documentation that bridges engineering and clinical interpretation.

• Collaborative, proactive communication with Data Science, Medical Affairs, and Product teams.

Joining us is more than saying “yes” to making the world a healthier place. It’s discovering a career that’s challenging, supportive and inspiring. Where a culture driven by excellence helps you not only meet your goals, but also create new ones. We focus on creating a diverse and inclusive culture, encouraging individual expression in the workplace and thrive on the innovative ideas this generates. If this sounds like the workplace for you, apply now! We commit to respond to every applicant.