As the AI/Machine Learning Engineer you will provide technical leadership within a vibrant, high-performing R&D team that is redefining the treatment of structural heart disease.
S/he will lead the development of innovative ML-enabled features on a novel device and cloud platform.
Requirements & Skills:
Bachelor’s degree in Computer Science, Data Science, Engineering, or similar with an emphasis in artificial intelligence and machine learning. Strongly prefer a Master’s or PhD with an emphasis on multimodal machine learning.
Minimum 2 years of relevant industry experience.
Proficiency in common AI/ML programming languages (at least one of them): Python (preferred), R, Julia, or MATLAB.
Proficiency with common libraries used for numeric manipulation and machine learning such as Numpy, Pandas, Scikit-Learn, Tensorflow, Keras, etc.
Hands-on experience with a variety of supervised, semi-supervised, and unsupervised machine learning methods.
Basic experience in exploratory data analysis and visualization.
Experience in preparation and manipulation of training and validation datasets, including data structuring, normalization, augmentation, and synthetic methods.
Understanding of conventional statistical methods and techniques.
Self-motivated with the ability to collaborate across multiple disciplines (e.g., electrical, software, systems) to meet program milestones.
Advanced degree (Master’s or PhD) in Computer Science, Data Science, Physics or Engineering.
Basic experience in analytics reporting and interactive visualizations.
Experience in image/video segmentation and classification.
Experience in transferring trained models to a production cloud environment, e.g., API implementation, and containerization.
Familiarity with ISO 13485, 14971, etc.
Experience in compiling and optimization techniques (e.g., quantization, pruning, compression) to deploy models on edge devices
Database creation/usage experience to manage, store, and query multimodal datasets (e.g. PostgreSQL, Mongo, etc.)
Experience in C/C++/C# family of coding languages.
Experience in model life cycle management within a distributed HPC environment such as Azure Databricks.
Experienced in the sequential phases of medical device development from early feasibility and VOC through development, test, commercial launch, and post-market support.
Experience in first-in-human and pivotal clinical trials.
Experience and/or familiarity with cardiac anatomy and interventional cardiology.
