Machine Learning Engineer (Remote)

About the Role

Join Gesund.ai in developing a privacy-centric MLOps platform tailored for data-driven enterprises within healthcare and life sciences. This platform is intended to support the comprehensive lifecycle of machine learning initiatives, aiming to expedite innovative medical research and deliver clinical-grade ML solutions to the market. The company maintains a rapidly growing network comprising early clinical and technology partners across the US, Israel, and Europe.

Responsibilities

• Conceptualize, design, and implement an MLOps platform centered on data management and federated learning from inception to deployment.
• Collaborate with the team to create tools and APIs for a centralized architecture that coordinates distributed agents and workers.
• Develop auxiliary software components enabling data scientists to efficiently interface with the platform.
• Facilitate integration with existing machine learning, deep learning, and federated learning libraries.
• Create scalable computer vision models addressing challenges such as medical image classification and segmentation.
• Build internal ML tools and pipelines to accelerate experimentation and iteration of models.
• Collaborate with fellow engineers to diagnose and resolve machine learning problems.

Requirements

• Proven experience in areas such as deep learning and computer vision.
• Familiarity with machine learning frameworks like TensorFlow, PyTorch, or YOLO.
• Avid interest and foundational understanding of Federated Learning and Self-Supervised Learning algorithms and their practical applications.
• Advanced proficiency in Python with emphasis on object-oriented programming principles.
• Strong skills in designing APIs using FastAPI.
• Comprehensive knowledge of deploying ML models using Docker and Kubernetes, capable of scaling across on-premises and cloud environments.