Note: The job is a remote job and is open to candidates in USA. Beacon Biosignals is transforming precision medicine for the brain, from clinical development to clinical care. They are seeking a Machine Learning engineer to work alongside a diverse team in developing and maintaining machine learning models that analyze brain and biosignal data for advancing therapy development.
Responsibilities
- Participate in and lead the entire biosignal-based algorithm development lifecycle for medical devices including specifications and requirements gathering, data curation and labeling, development, failure-analysis, production, maintenance, and documentation
- Select, implement, and develop the most appropriate method for each problem, knowing when to apply deep learning techniques and when other methods are more effective
- Enhance our internal deep learning and machine learning tools to boost team efficiency, introduce new model architectures and algorithmic techniques, and refine the codebase to encourage reusability where needed to enable rapid experimentation
- Spread and improve our best practices to ensure algorithm implementations are user-friendly, well-documented, and thoroughly tested, including unit tests, comprehensive documentation, CI, and non-regression testing
- Present results to key stakeholders and assist them in utilizing algorithms for client engagement
- Support the client-facing projects to understand and shape the impact Beacon algorithms have for our customers, both for existing deployed algorithms, and future algorithm development
Skills
- More than 4 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 and care about using the right tool for the job, which in many cases might not be machine learning or deep learning
- Follow and adopt best practices in software and ML engineering, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking
- 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 in a way that is appropriate for the audience at hand, both internally and externally
- Excited to participate in the entire algorithm development lifecycle, which spans scoping, data wrangling, algorithm development/experimentation, formal validation, quality/regulatory documentation, production deployment, and working with clients who might benefit from these algorithms
- Proficient in using PyTorch (preferred) or other deep learning frameworks for training, developing, and deploying deep learning models
- Familiar with latest Deep Learning advances (Transformer/ViT, large scale modeling, large model training, ...)
- Familiar with biosignals, medical imaging data, or large time-series datasets, or are enthusiastic about learning more in the domain
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