CloudPhysician ML Engineer-Lead

ML Engineer Lead

Role Overview

As an ML Engineering Lead Engineer, you will tackle significant challenges within the healthcare domain by implementing impactful computer vision and machine learning solutions. You will design and develop advanced algorithms, work with large-scale image and video datasets, and collaborate with cross-functional teams including operations, product, and medical experts. This role values innovation, continuous learning, and the application of cutting-edge research to real-world problems.

Key Responsibilities

• Design, develop, and implement computer vision models and machine learning algorithms for tasks such as image analysis, object detection, segmentation, and classification on real-time healthcare datasets.
• Mine, analyze, and preprocess large image and video datasets to drive optimization and extract valuable insights for computer vision applications.
• Develop interactive data visualizations and dashboards to provide real-time monitoring of model performance and key performance indicators.
• Rigorously track and enhance computer vision model performance through the definition of evaluation metrics, A/B testing methodologies, and thorough error analysis.
• Stay current with the latest research advancements and emerging frameworks such as PyTorch, and leverage strong proficiency in techniques including object detection (e.g., YOLO) and image segmentation (e.g., SAM).

What We Look For

Skills:

• Expertise in computer vision techniques and machine learning algorithms, with a strong emphasis on object detection, image segmentation, and advanced feature extraction.
• 2-4 years of hands-on experience in developing and implementing computer vision or machine learning solutions.
• Proficiency in Python along with experience in deep learning frameworks such as PyTorch and/or TensorFlow.
• Familiarity with OpenCV and other image processing libraries.
• Ability to work with large-scale image and video datasets, utilizing image preprocessing and data augmentation techniques to boost model performance.

Qualifications

• Required Experience: 2-4 years of relevant hands-on experience.
• Bonus Points: Familiarity with containerization technologies such as Docker, version control systems like Git, and exposure to JavaScript for potential integration with web-based applications or visualization tools.

What We Offer

• Competitive compensation and benefits package.
• Professional growth and career development opportunities.
• Collaborative and innovative work environment.