Senior Algorithm Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Algorithm Engineer based in United States. The Senior Algorithm Engineer will develop and productionize machine learning and deep learning algorithms for complex brain and biosignal data. You will work across the full algorithm lifecycle, from requirements gathering and data curation through validation, deployment, maintenance, and documentation. The role sits at the intersection of machine learning, digital signal processing, neuroscience, and clinical applications. You will collaborate closely with data scientists, neuroscientists, software engineers, clinicians, and client-facing teams to translate research into reliable real-world solutions. A key focus will be advancing algorithmic capabilities while strengthening reusable tooling, engineering practices, testing, and experimentation workflows. You will have the opportunity to influence how advanced algorithms support the development of therapies and diagnostics for neurological, psychiatric, and sleep disorders. This is a fully remote U.S. role for an experienced engineer who combines technical depth with curiosity, simplicity, collaboration, and a strong commitment to meaningful patient impact. 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 kn