Remote Deep Learning Engineer Jobs in Boston, MA

Beacon Biosignals is on a mission to revolutionize precision medicine for the brain. We are the leading at-home EEG platform supporting clinical development of novel therapeutics for neurological, psychiatric, and sleep disorders. Our FDA 510(k)-cleared Waveband EEG headband and AI algorithms enable quantitative biomarker discovery and implementation. Beacon's Clinico-EEG database contains EEG data from nearly 100,000 patients, and our cloud-native analytics platform powers large-scale RWD/RWE retrospective and predictive studies. Beacon Biosignals is changing the way that patients are treated for any disorder that affects brain physiology. 

Beacon Biosignals is seeking a Machine Learning engineer! 

At Beacon, we've found that cultural and scientific impact is driven most by those who lead by example. As such, we're always seeking out new contributors whose work demonstrates innate curiosity, a bias toward simplicity, an eye for composability, a self-service mindset, and-most of all-a deep empathy toward colleagues, stakeholders, users, and patients. We believe a diverse team builds more robust systems and achieves higher impact.

Beacon's robust asynchronous work practices ensure a first-class remote work experience, but we also have in-person office hubs in Boston, New York City and Paris.

What success looks like

• 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.

What you will bring

• You have 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.
• You are 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.
• You are proficient in using PyTorch (preferred) or other deep learning frameworks for training, developing, and deploying deep learning models.
• You are familiar with latest Deep Learning advances (Transformer/ViT, large scale modeling, large model training, ...)
• You follow and adopt best practices in software and ML engineering, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking.
• You are familiar with biosignals, medical imaging data, or large time-series datasets,  or are enthusiastic about learning more in the domain.
• You thrive in a team environment, recognizing that collaboration, open communication, and continuous feedback are essential for collective success.
• You are able to distill, discuss, and present complex technical topics in a way that is appropriate for the audience at hand, both internally and externally.
• You are 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.