Algorithm Engineer (AI)

TL;DR

Algorithm Engineer (AI): Building and deploying machine and deep learning algorithms for at-home EEG and cardiopulmonary biosignal data in precision medicine, with an accent on digital signal processing, model development, and regulated medical-device workflows. Focus on designing robust algorithms, validating them for production, and solving complex challenges across large time-series datasets, clinical impact, and algorithm reliability.

Location: Remote within the United States, with optional access to office hubs in Boston, New York City, and Paris.

Salary: $150,000–$170,000 base salary, plus equity, PTO, and other benefits.

Company

hirify.global develops at-home EEG and cardiopulmonary signal platforms for precision diagnostics, clinical development, and treatment of neurological, psychiatric, and sleep disorders.

What you will do

• Lead the end-to-end biosignal algorithm lifecycle, from requirements gathering and data curation to validation, production deployment, 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 model architectures and algorithmic techniques, and promote reusable code for rapid experimentation.
• Establish best practices for unit testing, documentation, continuous integration, and non-regression testing.
• Present technical results to stakeholders and support client-facing projects using deployed and future algorithms.

Requirements

• More than 4 years of industry experience in machine learning and deep learning, preferably in health sciences or another regulated field.
• Proven experience bringing algorithms into production and participating in formal validation and quality or regulatory documentation.
• Strong experience with digital signal processing and statistics, with the judgment to select non-ML methods when appropriate.
• Proficiency with PyTorch or another deep learning framework, including model training, development, and deployment.
• Knowledge of modern deep learning methods such as Transformers, vision transformers, large-scale modeling, and large-model training.
• Experience with software and ML engineering practices including testing, version control, code reviews, Docker, CI/CD, and experiment tracking.

Nice to have

• Experience with biosignals, medical imaging, or large time-series datasets.
• Background in neuroscience, clinical development, medical devices, or healthcare technology.
• Interest in expanding expertise in brain and cardiopulmonary physiology.

Culture & Benefits

• Asynchronous work practices support a remote-first experience.
• Collaboration with data scientists, neuroscientists, engineers, clinicians, stakeholders, and clients.
• Emphasis on curiosity, simplicity, composability, self-service, empathy, and continuous feedback.
• Benefits include equity, paid time off, and additional compensation benefits.