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Beacon Biosignals is transforming precision medicine for the brain through at-home EEG and biosignal platforms for clinical development and diagnostics. As an Algorithm Engineer on the analytics and machine learning domain, you will work alongside data scientists, neuroscientists, engineers, and clinicians to scope, build, deploy, and maintain machine and deep learning models that analyze brain and biosignal data for sleep, neurological, and psychiatric therapy development.
You will participate in and lead the entire biosignal-based algorithm development lifecycle for medical devices, including specifications gathering, data curation and labeling, development, failure analysis, production, maintenance, and documentation. You'll select and implement appropriate methods for each problem, knowing when to apply deep learning and when other techniques are more effective. You will enhance internal deep learning and machine learning tools to boost team efficiency, introduce new model architectures and algorithmic techniques, and refine the codebase for reusability and rapid experimentation.
You will spread and improve best practices to ensure algorithm implementations are user-friendly, well-documented, and thoroughly tested with unit tests, comprehensive documentation, CI/CD, and non-regression testing. You'll present results to key stakeholders and assist them in utilizing algorithms for client engagement, while supporting client-facing projects to understand and shape the impact of Beacon algorithms for customers—both for existing deployed algorithms and future development.
You bring more than 4 years of industry experience in machine learning and deep learning, particularly in health sciences or regulated fields, with a proven track record of bringing algorithms into production. You have strong experience with digital signal processing (DSP) and statistics, and care about using the right tool for the job. You are proficient in PyTorch or other deep learning frameworks, familiar with latest advances (Transformers, ViT, large-scale modeling), and follow best practices in software and ML engineering including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking. You are familiar with biosignals, medical imaging, or large time-series datasets, thrive in collaborative team environments, and can distill complex technical topics for diverse audiences.
