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Salary: USD 170,000 - 190,000 / 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. Your responsibilities include scoping, building, deploying, and maintaining machine and deep learning models that analyze brain and biosignal data for advancing sleep, neurological, and psychiatric therapy development.
Key responsibilities:
- 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 appropriate methods for each problem, knowing when to apply deep learning versus other techniques
- Enhance internal deep learning and machine learning tools to boost team efficiency and introduce new model architectures
- Establish and spread best practices to ensure algorithm implementations are user-friendly, well-documented, and thoroughly tested
- Present results to key stakeholders and assist in algorithm utilization for client engagement
- Support client-facing projects to understand and shape the impact of Beacon algorithms
Beacon values cultural and scientific impact driven by those who lead by example, seeking contributors who demonstrate curiosity, bias toward simplicity, composability, self-service mindset, and deep empathy toward colleagues, stakeholders, users, and patients. The company maintains office hubs in Boston, New York City, and Paris but this role is fully remote across the US.
Requirements:
- 5+ years of industry experience in machine learning and deep learning, particularly in health sciences or regulated fields, with proven track record of bringing algorithms into production
- Experience with digital signal processing (DSP) and statistics; understanding of when to use the right tool (which may not be ML/DL)
- Proficiency in PyTorch (preferred) or other deep learning frameworks for training, developing, and deploying models
- Proficiency with latest deep learning advances (Transformer/ViT, large scale modeling, large model training)
- Strong software and ML engineering practices including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking
- Experience with biosignals, medical imaging data, or large time-series datasets, or enthusiasm for learning in the domain
- Strong collaboration and communication skills; ability to distill and present complex technical topics appropriately for different audiences
- Excitement about participating in the entire algorithm development lifecycle from scoping through production deployment and client engagement
