Website Edwards Lifesciences
We are seeking for an exceptional and self-motivated Machine Learning Engineer to join our growing team in the Applied Machine Learning Group. The main responsibilities include applying machine learning, time series models, physiological signal processing, and human physiology experience to develop algorithms for minimally invasive and noninvasive critical care patient monitoring products.
- Data extraction, transformation, and loading from different data sources using SQL and AWS
- Create data tools for analytics, mining, and visualization and integrate them with pipelines for model development
- Strong theoretical and applied background in human physiology and anatomy is a plus
- Strong theoretical and applied background in Digital Signal Processing (DSP), mathematical modeling and algorithm development for biomedical applications is a plus
- Strong experience in scripting languages, such as MATLAB and Python for prototyping, testing and validation of signal processing algorithms and model creation.
- Impeccable documentation of work results. Experience in writing scientific reports and papers is a plus.
- Python data science pipeline (pandas, sklearn, numpy, scipy, TensorFlow)
- Database technologies (SQL or NoSQL) is a plus
- Experience with AWS cloud services is a plus
- Data modeling, analytics and visualization
- Machine learning and deep learning
- Experience working with high resolution time series data (ECG, arterial blood pressure, etc.)
- Physiological signal processing
- Knowledge of human physiology is a plus
- Internship applicants must be enrolled in an accredited Master’s, or PhD program for the entire duration of this internship. Upon graduating, students are no longer eligible for an internship at Edwards.
- Availability for an assignment for a minimum of 10 weeks over the course of the summer.
- Must be in good scholastic standing (3.0+ GPA or equivalent)
- Permanent residency is required; some exceptions will apply.
- A Master’s or PhD student in biomedical engineering, electrical engineering, or computer sciences with a strong background in mathematics, signal processing, and machine learning is preferred.
- Must be proactive and creative in achieving goals.