Principal Algorithm Engineer
Cypress HCMabout 1 month ago
Irvine, CA, United States
On-site
Full-time
Junior Level (1-3 years)
Job Description
Job Description
A medical device manufacturing client is seeking an exceptional and self-motivated Principal Algorithms Engineer to join a growing R&D team in Irvine, California. The ideal candidate will have strong statistical data analysis skills, extensive experience developing advanced algorithms, and the ability to independently drive projects from concept through implementation.
Responsibilities
- Lead the development of advanced algorithms for patient monitoring using machine learning and signal processing techniques.
- Perform statistical data analysis to characterize and validate algorithm performance.
- Collaborate with clinicians and other technical stakeholders to support clinical studies and contribute to scientific publications.
- Work cross-functionally with engineering, clinical, regulatory, and product teams to support regulatory submissions and product launches.
Qualifications
- PhD in Electrical Engineering, Computer Science, Biomedical Engineering, Statistics, or a related field.
- Strong programming skills in Python or MATLAB.
- Strong experience with deep reinforcement learning.
- Strong experience with machine learning and deep learning techniques, including CNNs, LSTMs, Transformers, and related architectures.
- Strong experience in digital signal processing, mathematical modeling, and algorithm development.
- Experience working with biomedical time-series data, such as arterial blood pressure, ECG, EEG, or similar physiological signals.
- Experience with control theory.
- Knowledge of patient monitoring technologies and human physiology.
Salary
Salary: $150,000 - $200,000 + bonus
Required Skills
Human Physiology
Control Theory
Transformers
Signal Processing
Statistical Data Analysis
LSTMs
Machine Learning
MATLAB
Mathematical Modeling
CNNs
Patient Monitoring Technologies
Python
Digital Signal Processing
Biomedical Time-Series Data
Algorithm Development
Deep Reinforcement Learning
Deep Learning