Senior Algorithm Engineer (Machine Learning)

Location: Fully remote, based anywhere in the United States. In-person office hubs are available in Boston, New York City, and Paris.

Salary: $170,000–$190,000 per year in the United States, plus equity, PTO, and other benefits.

Company

hirify.global develops at-home EEG and cardiopulmonary signal platforms for neurological, psychiatric, sleep, and central nervous system clinical development and diagnostics.

What you will do

• Lead the full biosignal-based algorithm development lifecycle for medical devices, from requirements and data curation through validation, production, maintenance, and documentation.
• Select and develop appropriate statistical, signal-processing, machine learning, and deep learning methods for each problem.
• Improve internal machine learning tools, introduce new model architectures, and refine reusable code for rapid experimentation.
• Establish best practices for tested, documented, maintainable algorithm implementations, including unit testing, CI, and non-regression testing.
• Present technical results to stakeholders and support client-facing projects involving deployed and future algorithms.

Requirements

• More than 5 years of industry experience in machine learning and deep learning, especially in health sciences or other regulated fields, with algorithms brought into production.
• Experience with digital signal processing, statistics, biosignals, medical imaging data, or large time-series datasets.
• Proficiency with PyTorch or another deep learning framework for training, developing, and deploying models.
• Knowledge of current deep learning advances, including Transformers, Vision Transformers, large-scale modeling, and large-model training.
• Experience with software and ML engineering practices such as testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking.
• Must be based anywhere in the United States; strong collaboration and technical communication skills are required.

Culture & Benefits

• Fully remote work practices with optional in-person office hubs.
• Collaboration with data scientists, neuroscientists, engineers, clinicians, stakeholders, and clients.
• Emphasis on curiosity, simplicity, composability, self-service, empathy, open communication, and continuous feedback.
• Compensation includes equity, paid time off, and additional benefits.