Senior Algorithm Engineer in New York at Jobgether

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.

• 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.

• 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.