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Salary: USD 170,000 - 220,000 / 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 Senior Algorithm Engineer, you will work within Beacon's analytics and machine learning domain alongside data scientists, neuroscientists, engineers, and clinicians. You'll scope, build, 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:
- 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
- Select and implement the most appropriate method for each problem, knowing when to apply deep learning and when other methods are more effective
- 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
- Spread and improve best practices to ensure algorithm implementations are user-friendly, well-documented, and thoroughly tested (unit tests, documentation, CI, non-regression testing)
- Present results to key stakeholders and assist them in utilizing algorithms for client engagement
- Support client-facing projects to understand and shape the impact of Beacon algorithms for customers, both for existing deployed algorithms and future development
Beacon values cultural and scientific impact driven by those who lead by example, demonstrating innate curiosity, bias toward simplicity, composability, self-service mindset, and deep empathy toward colleagues, stakeholders, users, and patients.
REQUIREMENTS:
- More than 5 years of industry experience in machine learning and deep learning, particularly in health sciences or other regulated fields, with a proven track record of bringing algorithms into production
- Experienced with digital signal processing (DSP) and statistics; ability to select the right tool for the job, which may not always be machine learning or deep learning
- Proficient in PyTorch (preferred) or other deep learning frameworks for training, developing, and deploying deep learning models
- Proficient with latest deep learning advances (Transformer/ViT, large scale modeling, large model training)
- Follow and spread best practices in software and ML engineering, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking
- Experienced with biosignals, medical imaging data, or large time-series datasets, or enthusiastic about learning in the domain
- Thrive in a team environment, recognizing that collaboration, open communication, and continuous feedback are essential for collective success
- Able to distill, discuss, and present complex technical topics appropriately for different audiences, both internally and externally
- Excited to participate in the entire algorithm development lifecycle spanning scoping, data wrangling, algorithm development/experimentation, formal validation, quality/regulatory documentation, production deployment, and client collaboration
