Eko builds AI and digital tools to enable every healthcare provider to more accurately detect heart and lung disease – the leading causes of death globally. Our FDA cleared, industry leading products are used by hundreds of thousands of clinicians on millions of patients around the world. With Eko, clinicians can detect cardiac and pulmonary disease with higher accuracy, diagnose with more confidence, manage treatment effectively, and ultimately give their patients the best care possible.
We have strong venture capital backing from investors like Artis Ventures, Questa Capital, Highland Capital, and Mayo Clinic Ventures.
Recognized by TIME magazine in 2025 as one of the world’s top healthcare technology companies, Eko is one of the fastest growing digital health companies with products used around the world at some of the most prestigious health systems.
We have more than 8 FDA clearances including novel AI algorithms, and we invest heavily in quality clinical research and R&D to build and validate exceptional products for patients we care deeply about.
We’ve built a mission driven, high performing, talented, and diverse team of engineers, physicians, PhD’s, creatives, and technologists. We are committed to investing in each other and our mission to ensure all patients have access to high quality care.
We are headquartered in Emeryville, California and privately-held with world class investors and partners.
Design, develop, and validate signal processing algorithms for cardiac and pulmonary sounds — including filtering, noise reduction, artifact removal, segmentation, and feature extraction
Architect and deploy on-device machine learning models targeting resource-constrained microcontrollers, balancing accuracy against memory footprint, latency, and power consumption
Own the full algorithm lifecycle: from clinical requirements and dataset curation through model training, embedded deployment, and post-market performance monitoring
Collaborate closely with firmware engineers to integrate algorithm pipelines into real-time embedded systems
Partner with clinical and data science teams to define ground-truth labeling strategies, evaluate model performance on clinically relevant populations, and translate findings into algorithm improvements
Contribute to on-device model optimization techniques including training and deployment via frameworks such as TensorFlow Lite.
Develop robust offline and hardware-in-the-loop test benches using Python to validate algorithm correctness, regression-test against acoustic ground truth, and characterize edge-case behavior
Author and maintain algorithm documentation, design history files, and verification & validation records in compliance with FDA software development regulations and IEC 62304
Conduct rigorous algorithm code reviews and establish best practices for numerical stability, reproducibility, and embedded portability across the product portfolio
7+ years of experience developing and deploying signal processing algorithms in production systems
Strong theoretical foundation in DSP: filter design (FIR/IIR), spectral analysis and time-frequency methods (e.g. STFT)
Hands-on experience training and deploying ML/DL models (CNNs, RNNs, transformers) for time-series or audio classification tasks
Proven ability to optimize and deploy models to embedded targets — familiarity with TFLM or equivalent embedded inference frameworks
Proficiency in Python (NumPy, SciPy, PyTorch or TensorFlow) for algorithm development and data analysis
Working knowledge of C/C++ sufficient to write, review, and debug embedded algorithm implementations alongside firmware engineers
Experience with model compression techniques
Solid understanding of real-time constraints
Comfort working with raw physiological or acoustic signals and domain-specific data quality challenges (motion artifact, ambient noise, sensor variability)
Degree in a related field preferred but not required
Experience in an FDA-regulated medical device environment (design controls, IEC 62304, 510(k) process)
Background in cardiac or respiratory audio signal processing — PCG, ECG, lung sounds, or related biosignals
Familiarity with clinical validation methodology: sensitivity/specificity analysis, ROC curves etc. for algorithm performance claims
Familiarity with MLOps practices for embedded targets: model versioning, automated retraining pipelines, and over-the-air model update strategies
Previous work in a startup environment where you have worn multiple hats and iterated rapidly from prototype to clinical-grade product
Publications, patents, or open-source contributions in signal processing, biomedical ML, or embedded AI are a plus
Familiarity with coding assistant tools like Claude Code for accelerated development
We believe the ability to listen is the ability to care. Eko was named one of the World’s Top HealthTech Companies of 2025 by TIME and Statista. This honor reflects the mission to transform cardiac and pulmonary care through AI-powered stethoscopes and digital health solutions.
Eko Health is a leading digital health company advancing how healthcare professionals detect and monitor heart and lung disease with its portfolio of digital stethoscopes, patient and provider software, and AI-powered analysis. Its FDA-cleared platform, with over 650,000 devices sold to clinicians globally, allows them to detect earlier and with higher accuracy, manage treatment effectively, and ultimately give their patients the best care possible. Eko Health is headquartered in Emeryville, California, with over $165 million in funding from ARTIS Ventures, DigiTx Partners, Double Point Ventures, EDBI, Highland Capital Partners, LG Technology Ventures, Mayo Clinic, Morningside Technology Ventures Limited, NTTVC, Questa Capital, and others. For more information, visit www.ekohealth.com.
