Senior Algorithm Engineer

Accountabilities:

• Lead and contribute to the complete development lifecycle for biosignal-based algorithms used in medical applications, including requirements gathering, specifications, data curation, labeling, experimentation, validation, production deployment, maintenance, and documentation.
• Select and implement appropriate analytical approaches for each problem, applying machine learning and deep learning when they provide the best solution while leveraging statistical and signal-processing techniques when more suitable.
• Develop, train, validate, and deploy machine learning and deep learning models for brain, EEG, and other biosignal datasets.
• Introduce and evaluate new model architectures and algorithmic techniques, including modern deep learning approaches, while improving the internal tooling used for experimentation and model development.
• Enhance reusable machine learning infrastructure and codebases to enable efficient experimentation, composability, maintainability, and rapid iteration.
• Establish and promote strong software and ML engineering practices, including unit testing, code reviews, version control, CI/CD, Dockerization, experiment tracking, documentation, and non-regression testing.
• Conduct data analysis, model evaluation, failure analysis, and performance investigations to ensure algorithms are robust and production-ready.
• Present technical findings and algorithm performance to internal stakeholders and support teams in applying algorithmic capabilities to client-facing engagements.
• Partner with client-facing teams to understand customer needs and assess the real-world impact of deployed and future algorithms.
• Contribute to formal validation and quality or regulatory documentation required for medical-device and healthcare applications.
• Collaborate across multidisciplinary teams to scope new opportunities, resolve technical challenges, and translate complex scientific and technical concepts into practical solutions.
• Contribute to a culture of knowledge sharing, continuous improvement, technical curiosity, and empathy toward colleagues, stakeholders, users, and patients.

Requirements:

• 5+ years of industry experience in machine learning and deep learning, preferably within health sciences, medical technology, or another regulated environment, with a demonstrated record of bringing algorithms into production.
• Strong understanding of digital signal processing (DSP), statistics, and algorithm development, with the judgment to select the right methodology rather than defaulting to machine learning.
• Advanced hands-on experience with PyTorch or another modern deep learning framework for model development, training, evaluation, and deployment.
• Strong knowledge of contemporary deep learning techniques, including Transformers, Vision Transformers (ViT), large-scale modeling, and large-model training.
• Experience applying machine learning to biosignals, medical imaging, or large-scale time-series datasets is strongly preferred; candidates with adjacent experience and a strong interest in biosignals are encouraged to apply.
• Strong software and ML engineering practices, including testing, version control, code reviews, documentation, Docker, CI/CD, and experiment tracking.
• Experience working across the full algorithm lifecycle, including data preparation, experimentation, formal validation, production deployment, maintenance, and documentation.
• Ability to communicate complex technical concepts clearly and adapt presentations to technical, scientific, clinical, business, and client-facing audiences.
• Strong collaborative skills and the ability to work effectively with data scientists, engineers, neuroscientists, clinicians, and other cross-functional stakeholders.
• A self-directed, curious, and pragmatic approach, with a bias toward simplicity, reusable solutions, continuous learning, and high-quality execution.
• Bachelor’s or advanced degree in computer science, machine learning, electrical engineering, biomedical engineering, statistics, a related technical discipline, or equivalent practical experience.

Benefits:

• Salary: $170,000–$190,000 base salary for U.S.-based candidates, depending on experience, skills, and location.
• Total compensation: Base salary complemented by equity, paid time off, and additional benefits.
• Work arrangement: Fully remote position based anywhere in the United States.
• Access to optional in-person office hubs in Boston, New York City, and Paris.
• Opportunity to work at the intersection of machine learning, neuroscience, biosignals, and clinical applications.
• Meaningful opportunity to contribute to technologies supporting neurological, psychiatric, and sleep disorder research and care.
• Collaboration with multidisciplinary teams spanning data science, neuroscience, engineering, clinical expertise, and customer engagement.
• Exposure to advanced deep learning methods and large-scale biosignal datasets.
• An environment emphasizing curiosity, simplicity, composability, self-service, collaboration, and continuous improvement.
• Inclusive culture that values diverse perspectives and recognizes their contribution to stronger systems and greater impact.