EnChannel Medical is a high-tech medical device research and development company dedicated to developing next-generation technologies and products for the treatment of atrial fibrillation, providing cardiac electrophysiologists and patients with atrial fibrillation with simpler, safer and more efficient diagnosis, treatment and management. The company has three subsidiaries, two in China and one the USA (Ladera Ranch, CA), which undertake different R&D and manufacturing tasks of the company.

Our Algorithm team is looking for an Applied Scientist to work on developing research and product features for our mapping system. You will be responsible for designing, developing, testing, and improving machine learning (ML) algorithms for all technology subsystems. The Applied Scientist will use state-of-the-art ML to build advanced algorithmic systems such as object detection, segmentation, and classification to improve clinical workflow. This scientist will have the opportunity to design and build end-to-end solutions. The ideal candidate is detailed oriented with a desire to work in a fast-paced start-up environment while handling multiple priorities.

Essential Functions

• Investigate and implement innovative real-time applications and algorithms of computer vision, time series, and graphical data for cardiac signals.
• Analyze and interpret complex cardiac signals using machine learning and statistical techniques.
• Model complex problems, discover insights, and identify opportunities using statistical, algorithmic, and visualization techniques for intracardiac electrograms.
• Evaluation, adoption, and refinement of prototype algorithms developed by our engineers, scientists, and consultants.
• Collaborate with the software engineering team to implement algorithms into computationally efficient, “real-time” operations.
• Maintain, update, and document design requirements throughout the entire system life cycle.
• Collaborate with cross-functional teams to develop and implement ML solutions.
• May be required to actively contribute to regulatory filings, patent applications and other industry related publications.

Requirements

• Must be able to work onsite in a fast paced “start-up” culture.
• Strong interest in applying ML to medical problems.
• Strong programming skills in languages such as Python, C++, and MATLAB.
• Experience with ML libraries such as OpenCV, TensorFlow, and PyTorch.
• Broad understanding of machine learning, deep learning, and statistical techniques.
• Expertise in at least one of these: computer vision, time series analysis, self-supervised learning, multi-model ML.
• 2+ years of practical Experience in developing ML solutions in a production environment especially in the medical domain.
• A bachelor’s and/or master's degree, and/or PhD in scientific/engineering discipline; or equivalent combination of education and experience.
• (preferred) Experience with use of ML in cardiac anatomy and electrophysiology, ECG, unipolar/bipolar electrogram.
• (preferred) Experience in developing computer vision algorithms for CT, MRI, ultrasound images.

What you bring

• Ability to communicate effectively with Scientist and Engineers.
• Working with high quality standards and be pro-active in finding solutions to achieve successful outcomes.

What we offer

• Amazing people and culture.
• Competitive Salary.
• Comprehensive benefits plan and 401K with company matching.
• Casual dress and start-up work environment.
• Wellness and fitness programs.

Our pay ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum new hire pay for the position located in California. Within the range, individual pay is determined by location, additional factors, including job-related skills, experience, and relevant education or training.

EnChannel Medical is an equal opportunity employer. We believe in hiring a diverse workforce and sustaining an inclusive, people-first culture. We are committed to non-discrimination on any protected basis, such as disability and veteran status, or any basis covered under acceptable law.