Algorithm Engineer

Role overview

An algorithm engineering role focused on biosignal analysis for advancing sleep, neurological, and psychiatric therapy development. The position partners with neuroscientists, clinicians, data scientists, and engineers to scope, build, deploy, and maintain machine learning and deep learning models that analyze brain and physiological data. The work covers the full medical device algorithm lifecycle, from early specification through validation, production, and ongoing maintenance.

Responsibilities

- Lead end-to-end biosignal algorithm development for medical devices, including requirements gathering, data curation and labeling, development, failure analysis, production deployment, maintenance, and documentation. - Select the most appropriate method for each problem, applying deep learning where it fits and other techniques where they are more effective. - Strengthen internal machine learning and deep learning tooling to improve team efficiency, introduce new architectures, and refine the codebase for reusability and rapid experimentation. - Raise engineering quality through clear documentation, unit tests, CI pipelines, and non-regression testing. - Present results to key stakeholders and support them in applying algorithms within client engagements. - Contribute to client-facing projects to understand and shape the impact of deployed algorithms and guide future development.

Requirements

- More than four years of industry experience in machine learning and deep learning, ideally in health sciences or other regulated fields, with a track record of taking algorithms to production. - Solid background in digital signal processing and statistics, with the judgment to choose the right tool rather than defaulting to ML or DL. - Proficiency with PyTorch (preferred) or comparable deep learning frameworks for training, development, and deployment. - Familiarity with recent advances such as Transformer and ViT architectures, large-scale modeling, and 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 strong enthusiasm to learn the domain. - Ability to distill and present complex technical topics for varied internal and external audiences, and comfort collaborating across open, feedback-driven teams.

Benefits and work setup

- Fully remote within the United States, supported by asynchronous work practices designed for a first-class remote experience. - Optional in-person collaboration at office hubs in Boston, New York City, and Paris. - US-based salary range of $150,000–$170,000, with adjustments based on experience, skills, and location. - Total compensation package includes equity, PTO, and additional benefits.