Algorithm Engineer
Develop and productionize machine- and deep-learning algorithms for biosignal and EEG data used in medical devices, clinical development, and diagnostics. The role requires 4+ years of industry experience, DSP and statistics expertise, PyTorch proficiency, and familiarity with regulated environments and production ML practices.
About the job
Responsibilities
- Lead the full biosignal-based algorithm development lifecycle for medical devices, including requirements gathering, data curation and labeling, development, failure analysis, production, maintenance, and documentation.
- Select, implement, and develop appropriate statistical, signal-processing, machine-learning, and deep-learning methods.
- Improve internal machine-learning and deep-learning tools, model architectures, algorithmic techniques, and reusable code to enable rapid experimentation.
- Establish best practices for user-friendly, documented, and tested algorithm implementations, including unit tests, continuous integration, and non-regression testing.
- Present results to stakeholders and support client engagement and client-facing projects.
Requirements
- More than 4 years of industry experience in machine learning and deep learning, particularly in health sciences or regulated fields, with experience bringing algorithms into production.
- Experience with digital signal processing and statistics.
- Proficiency with PyTorch or other deep-learning frameworks for training, developing, and deploying models.
- Familiarity with current deep-learning advances, including Transformers, Vision Transformers, large-scale modeling, and large-model training.
- Software and machine-learning engineering best practices, including testing, version control, code reviews, documentation, Docker, CI/CD, and experiment tracking.
- Familiarity with biosignals, medical imaging data, or large time-series datasets, or enthusiasm for learning the domain.
- Strong collaboration, communication, and technical presentation skills.
- Ability to participate in scoping, data wrangling, experimentation, formal validation, quality and regulatory documentation, production deployment, and client engagement.
Compensation and Benefits
- US-based salary range: $150,000–$170,000.
- Compensation includes equity, paid time off, and other benefits.
Skills
Machine Learning, Deep Learning, Digital Signal Processing, Statistics, PyTorch, Transformers, Vision Transformers, Python, Docker, CI/CD, Experiment Tracking, Medical Imaging, Time-Series Analysis
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