Machine Learning Engineer

Overview

ml Health 360 is Health-tech company. We are committed to revolutionizing and enable the healthcare industry by providing innovative tools and resources that empower healthcare professionals and institutions. Our current focus is around leveraging the power of deep learning technology for image screening to improve patient outcomes, reduce costs, and increase efficiency.

As an ML Engineer at ml Health 360
, you will be a hands-on technical driver responsible for the end-to-end development of our AI-powered medical diagnostic tools. This is a hybrid role for a practitioner who is equally comfortable writing research-grade deep learning code and production-grade data infrastructure. You will be responsible for building, training, and deploying high-performance models that directly impact patient care, moving seamlessly between R&D experimentation and robust engineering execution.

• Design, implement, and train deep learning models (specifically for image segmentation and classification) using PyTorch or Tensor Flow.
• Build and maintain end-to-end data pipelines to ingest, process, and version large-scale medical imaging datasets (DICOM/NIfTI).
• Wrap models into scalable microservices using Docker and Kubernetes, ensuring low-latency inference in clinical environments.
• Clean, augment, and manage high-dimensional radiological data, working directly with medical annotation tools and clinical datasets.
• Profile and optimize model performance (quantization, pruning, or hardware acceleration) for efficient deployment on cloud or edge infrastructure.
• Code the interfaces and APIs required to integrate AI outputs into clinical workflows and web-based UI ecosystems.
• Implement MLOps practices, including automated testing, model monitoring, and continuous retraining (CT) loops.

• 3+ years of hands-on experience building and deploying machine learning models, with a strong emphasis on product ionizing research.
• 1+ years of experience in medical image analysis, with deep technical knowledge of CT, MRI, or X-ray data structures and radiological workflows.
• Expert-level Python skills with a focus on writing high-performance, maintainable code.
• Extensive experience with PyTorch, Tensor Flow, or Keras, specifically applied to computer vision and volumetric data.
• Practical, hands-on experience with Docker, Kubernetes, and ML orchestration tools (e.g., MLflow, Kubeflow, or Prefect).
• Direct experience managing cloud-based training environments (AWS, Azure, or GCP) and optimizing GPU utilization.
• Understanding of data privacy and security requirements (HIPAA/PIPEDA) as they relate to engineering architecture.

Master’s or Ph.D. in Computer Science, Biomedical Engineering, Data Science, or a related field—with a demonstrable portfolio of shipped machine learning products.