Principal Algorithms Engineer - Biomedical ML & Signals

Cypress HCM; Irvine (CA); On-site; USD 150,000 - 200,000; Full time

Job summary

Cypress HCM is seeking a Principal Algorithms Engineer for a medical device client in Irvine, CA. The role centers on developing advanced algorithms for patient monitoring using machine learning and signal processing, with ownership from concept to implementation.

The ideal candidate will have a PhD and expertise in deep reinforcement learning, ML/DL techniques (CNNs, LSTMs, Transformers), and biomedical time-series data, collaborating with clinicians and regulatory teams.

Qualifications

• PhD in a related field is required.
• Strong programming skills in Python or MATLAB.
• Experience with deep reinforcement learning.
• Experience with ML/DL techniques including CNNs, LSTMs, Transformers.
• Experience with digital signal processing and mathematical modeling.
• Experience with biomedical time-series data (e.g., ECG, EEG).
• Knowledge of patient monitoring technologies and physiology.

Responsibilities

• Lead development of advanced algorithms for patient monitoring using ML and signal processing.
• Perform statistical data analysis to characterize and validate algorithm performance.
• Collaborate with clinicians and other stakeholders on clinical studies and publications.
• Work cross-functionally with engineering, clinical, regulatory, and product teams to support regulatory submissions and product launches.

Skills

Deep reinforcement learning

Machine learning

Digital signal processing

Time-series data analysis

Statistical data analysis

Control theory

Education

PhD in Electrical Engineering, Computer Science, Biomedical Engineering, Statistics, or related field

Tools

Python

MATLAB

CNNs

LSTMs

Transformers

Job description

Cypress HCM is seeking a Principal Algorithms Engineer for a medical device client in Irvine, CA. The role centers on developing advanced algorithms for patient monitoring using machine learning and signal processing, with ownership from concept to implementation.

The ideal candidate will have a PhD and expertise in deep reinforcement learning, ML/DL techniques (CNNs, LSTMs, Transformers), and biomedical time-series data, collaborating with clinicians and regulatory teams.