mlHealth 360
Website:
mlhealth360.com
Job details:
About mlHealth 360
mlHealth 360 is a Health-Tech company dedicated to revolutionizing healthcare
through deep learning-powered medical image screening. We empower
healthcare professionals and institutions with innovative AI tools to improve
patient outcomes, reduce costs, and enhance efficiency. Our focus is on
developing state-of-the-art models (CNNs, Transformers, LLMs, and VLMs) for
automated diagnosis, segmentation, and clinical decision support.
Role Description
As an ML Engineer at mlHealth 360, you will be a hands-on technical leader,
driving the end-to-end development of AI-powered diagnostic tools. This role
requires expertise in research-grade deep learning and production-grade
MLOps to deploy scalable, high-performance models in clinical settings. You will
bridge R&D experimentation and engineering execution, ensuring models are
clinically robust, efficient, and integrated into real-world workflows.
Key Responsibilities
1. Deep Learning Model Development
• Design, implement, and train advanced architectures (U-Net, CNN, ViT, 3D CNNs/Transformers, VLM, LLM) for segmentation, Report Generation, detection, classification, and multi-modal analysis in medical imaging.
• Implement and adapt state-of-the-art algorithms and model architectures based on cutting-edge research papers.
• Apply self-supervised learning (e.g., contrastive learning), reinforcement
learning (RL), and diffusion models for robust and adaptive solutions.
• Optimize models for high accuracy, low latency, and clinical interpretability using techniques like LoRA, adapter tuning, attention mechanisms, and multi-modal fusion (imaging + clinical text/EHRs).
• Develop solutions for 3D volumetric data (CT, MRI) and real-world clinical deployment.
2. End-to-End Data Pipelines
• Build and maintain scalable pipelines for ingesting, preprocessing, and versioning DICOM/NIfTI datasets (CT, MRI, X-ray).
• Automate data augmentation, normalization, and annotation using tools like Encord, MONAI, and OpenCV.
• Ensure data privacy and compliance with HIPAA/GDPR standards.
3. MLOps & Deployment
• Containerize models using Docker and deploy them on Kubernetes for scalable, low-latency inference in clinical environments.
• Implement MLOps best practices: automated testing, model monitoring, continuous training (CT) loops, and A/B testing.
• Optimize models for edge deployment (quantization, pruning, ONNX/TensorRT acceleration).
4. Research & Innovation
• Stay updated with SOTA research in medical imaging (e.g., foundation models, diffusion models) and prototype novel solutions.
• Publish findings in conferences/journals and contribute to open-source projects.
Required Experience
• 2+ years of hands-on experience in building and deploying ML models, with a focus on medical imaging or computer vision.
• 1+ year of experience in medical image analysis (CT, MRI, X-ray) and radiological workflows.
• Proficiency in:
o Deep Learning Frameworks: PyTorch, TensorFlow.
o Medical Imaging Libraries: MONAI, ITK, SimpleITK, pydicom.
o Annotation Tools: Encord, Labelbox, CVAT.
o MLOps & Cloud Platforms: AWS, Azure, Databricks, Docker, MLflow, Kubernetes.
o Production Deployment: Model optimization (quantization, ONNX, TensorRT), edge deployment, and scalable inference.
Education
• Bachelor's, Master's in Computer Science, Biomedical Engineering, Data Science, or a related field, with a portfolio of deployed ML projects in healthcare (e.g., GitHub, research publications, or product demos).
Why Join mlHealth 360?
• Work on impactful AI solutions that directly improve patient care.
• Collaborate with cross-functional teams (engineers, clinicians, researchers).
• Access to cutting-edge tools, datasets, and research opportunities.
• Growth opportunities: Mentorship, conferences, and access to cutting-edge research in medical AI.
Click on Apply to know more.