Senior Algorithm Engineer (Machine Learning)

Location: Primarily remote for candidates based near Paris, France, with optional access to office hubs in Paris, Boston, and New York City.

Company

hirify.global develops at-home EEG and cardiopulmonary monitoring platforms for precision medicine, neurological and psychiatric therapy development, and sleep and central nervous system diagnostics.

What you will do

• Lead and contribute to the full biosignal-based algorithm lifecycle, from requirements and data curation through validation, production, maintenance, and documentation.
• Select and implement appropriate statistical, signal-processing, machine learning, or deep learning methods for each problem.
• Develop and improve model architectures, internal ML tools, reusable code, and experimentation workflows.
• Apply software and ML engineering practices including testing, documentation, CI, non-regression testing, and code quality improvements.
• Present technical results to stakeholders and support client-facing projects involving 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.
• Experience with digital signal processing, statistics, biosignals, medical imaging, or large time-series datasets.
• Proficiency with PyTorch or another deep learning framework, including model training, development, and deployment.
• Knowledge of current deep learning advances such as Transformers, Vision Transformers, large-scale modeling, and large-model training.
• Experience with testing, version control, code reviews, documentation, Docker, CI/CD, and experiment tracking.

Nice to have

• Hands-on experience with EEG, cardiopulmonary signals, or other medical-device data.
• Experience working with clinical, neuroscience, or healthcare stakeholders.

Culture & Benefits

• Asynchronous practices support a strong remote-work experience.
• Work alongside data scientists, neuroscientists, engineers, clinicians, stakeholders, and clients.
• Emphasis on curiosity, simplicity, composability, self-service, collaboration, and empathy.
• Compensation includes base salary, equity, paid time off, and other benefits.