Senior Algorithms Engineer

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