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Salary: USD 150,000 - 170,000 / annual
Beacon Biosignals is transforming precision medicine for the brain through at-home EEG and biosignal platforms for clinical development and diagnostics. The company combines EEG and cardiopulmonary signals to deliver reimbursable assessments for sleep and central nervous system disorders.
As a Machine Learning Engineer, you'll join the analytics and machine learning domain, working alongside data scientists, neuroscientists, engineers, and clinicians to develop, deploy, and maintain machine and deep learning models that analyze brain and biosignal data for advancing sleep, neurological, and psychiatric therapy development.
Key responsibilities include:
- Lead the entire biosignal-based algorithm development lifecycle for medical devices, from specifications and data curation through production, maintenance, and documentation
- Select and implement appropriate methods for each problem, knowing when to apply deep learning versus other techniques
- Enhance internal ML/DL tools, introduce new model architectures, and refine codebases for reusability and rapid experimentation
- Establish and spread best practices ensuring algorithms are user-friendly, well-documented, and thoroughly tested (unit tests, CI/CD, non-regression testing)
- Present results to stakeholders and support client-facing projects to understand algorithm impact
- Contribute to both existing deployed algorithms and future algorithm development
You bring 4+ years of industry ML/DL experience, ideally in health sciences or regulated fields, with a proven track record of production deployments. You have strong expertise in digital signal processing and statistics, proficiency with PyTorch or similar frameworks, and familiarity with modern deep learning advances (Transformers, ViT, large-scale modeling). You follow software and ML engineering best practices including testing, version control, CI/CD, and experiment tracking. Experience with biosignals, medical imaging, or large time-series datasets is valued. You thrive in collaborative environments, communicate complex technical topics clearly to diverse audiences, and are excited about the full algorithm development lifecycle from scoping through client engagement.
