Job description We are seeking a highly skilled and experienced Machine Learning and Data Engineer to join our dynamic team. The ideal candidate will be responsible for designing, developing, and maintaining data pipelines and machine learning models, ensuring optimal performance and scalability. They will work closely with data scientists, software engineers, and business stakeholders to implement data-driven solutions and deploy machine learning models in production environments.

Responsibilities

Clinical Data Pipelines: Design and develop scalable ETL processes specifically for medical imaging (DICOM, NIfTI) and EHR data, ensuring seamless ingestion from PACS/RIS systems. Medical AI Deployment: Build and deploy state-of-the-art computer vision and NLP models, focusing on high-accuracy anomaly detection, and lesion segmentation. Regulatory-Grade Optimization: Maintain and optimize ML models to meet strict clinical performance benchmarks and FDA/CE regulatory requirements. Zero-Cloud & Edge Architecture: Collaborate on "zero-cloud" and on-premise AI deployments to satisfy hospital data privacy and residency requirements. Medical Data Preprocessing: Perform specialized image preprocessing (normalization, resampling, intensity scaling) and manage "test set" integrity for iterative model feedback loops. Quality & Compliance: Implement rigorous data validation and monitoring to ensure data integrity, focusing on bias detection and clinical drift. MLOps for Healthcare: Automate model training and versioning using MLOps practices that ensure full traceability—a critical requirement for medical device certification. Documentation: Maintain comprehensive technical documentation for model architecture and data provenance to support regulatory submissions (FDA 510(k), etc.).

Qualifications Education: Bachelor’s or Master’s degree in Computer Science, Biomedical Engineering, or a related field. Experience: 5+ years in data/ML engineering, with preference for experience in healthcare or Life Sciences. Core Tech Stack: Proficiency in Python; deep expertise in PyTorch or TensorFlow (specifically for 2D/3D computer vision). Medical Imaging Domain: Strong familiarity with DICOM standards, and libraries such as Pydicom, SimpleITK, or MONAI. Data Engineering: Expertise in SQL/NoSQL and data processing tools; experience handling large-scale unstructured image datasets. Cloud & Deployment: Experience with AWS/GCP/Azure and containerization (Docker, Kubernetes). Familiarity with deploying models in air-gapped or restricted hospital environments is a major plus. Compliance Knowledge: Deep understanding of HIPAA, GDPR, and data privacy standards. Knowledge of ISO 13485 or software as a medical device (SaMD) frameworks is highly desirable. MLOps: Hands-on experience with tools like MLflow or Kubeflow to manage the lifecycle of clinical models. Soft Skills: Ability to communicate complex technical concepts to both engineering peers and clinical stakeholders.

Why Join Us? Join a mission-driven team where your work directly will transform radiology through AI. mlHealth 360 is a global healthtech company transforming radiology with secure, FDA-cleared AI solutions. We go beyond simple image screening to provide the intelligence layer for modern hospitals. By leveraging deep learning to triage critical cases in seconds, we empower healthcare professionals to prioritize urgent care, reduce diagnostic costs, and dramatically improve patient outcomes. We offer a dynamic startup environment with opportunities with professional growth in the rapidly evolving field of digital health and AI regulation.