Algorithm Engineer
Beacon Biosignals12 days ago
Remote, EMEA or Paris, FranceMid Level
Responsibilities
- Lead and participate in the full biosignal-based algorithm development lifecycle for medical devices, from requirements gathering and data curation through production, maintenance, and documentation.
- Select, implement, and develop appropriate statistical, signal-processing, machine learning, and deep learning methods for each problem.
- Enhance internal machine learning and deep learning tools, introduce new model architectures and algorithmic techniques, and improve code reusability for rapid experimentation.
- Establish user-friendly, documented, and thoroughly tested algorithm implementations, including unit testing, continuous integration, and non-regression testing.
- Present results to stakeholders and help them use algorithms for client engagement.
- Support client-facing projects and assess the impact of deployed and future algorithms for customers.
Requirements
- More than 4 years of industry experience in machine learning and deep learning, particularly in health sciences or another regulated field, with experience bringing algorithms into production.
- Experience with digital signal processing and statistics, including judgment about when machine learning or deep learning is not the appropriate approach.
- Proficiency with PyTorch or other deep learning frameworks for training, developing, and deploying deep learning models.
- Familiarity with current deep learning advances, including Transformers, Vision Transformers, large-scale modeling, and large model training.
- Experience with software and ML engineering practices such as testing, version control, code reviews, documentation, Dockerization, continuous integration and delivery, and experiment tracking.
- Familiarity with biosignals, medical imaging data, or large time-series datasets, or enthusiasm for learning the domain.
- Ability to communicate and present complex technical topics appropriately to internal and external audiences.
- Willingness to participate across scoping, data wrangling, experimentation, formal validation, quality and regulatory documentation, production deployment, and customer collaboration.
- Ability to collaborate effectively with data scientists, neuroscientists, engineers, clinicians, stakeholders, and clients.
Benefits
- Remote work is supported, with in-person office hubs in Boston, New York City, and Paris.
- The total compensation package includes equity, paid time off, and other benefits.
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