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Medical Imaging Segmentation Jobs (NOW HIRING)

Actalent

Biomedicat Technician

Weston, FL · On-site

$20/hr

Experience using Materialise MIMICS or similar medical imaging/segmentation software. * Knowledge of Adobe Photoshop. * Certifications or related experience in radiology (CT, X-ray, MRI)

New

Jj

Principal Scientist, Imaging Analytics

Raritan, NJ · On-site

Hands-on expertise across the medical imaging AI stack: deep learning (segmentation, detection, classification, registration), radiomics, and multimodal predictive modeling. * Proficiency in Python ...

Jj

Principal Scientist, Imaging Analytics

Cambridge, MA · On-site

Hands-on expertise across the medical imaging AI stack: deep learning (segmentation, detection, classification, registration), radiomics, and multimodal predictive modeling. * Proficiency in Python ...

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Medical Imaging Segmentation information

What is medical imaging segmentation?

Medical imaging segmentation is the process of partitioning medical images, such as CT, MRI, or ultrasound scans, into meaningful regions or structures. This technique helps identify and isolate specific tissues, organs, or abnormalities, making it easier for clinicians to analyze and diagnose medical conditions. Segmentation can be done manually, semi-automatically, or automatically using advanced algorithms, including artificial intelligence. It is a critical step in many medical applications, such as treatment planning, disease monitoring, and surgical navigation.

What are the key skills and qualifications needed to thrive as a medical imaging segmentation specialist, and why are they important?

To excel in Medical Imaging Segmentation, you need a solid background in biomedical engineering, computer science, or related fields, with expertise in image analysis and anatomy. Familiarity with medical imaging software (such as 3D Slicer or ITK-SNAP), programming languages (like Python), and machine learning frameworks is typically required. Attention to detail, strong problem-solving skills, and effective communication are critical soft skills for accurately interpreting images and collaborating with clinical teams. These skills ensure precise, reliable segmentation that supports diagnostics, research, and treatment planning in medical settings.

What are some common challenges faced in medical imaging segmentation roles, and how can they be addressed?

Professionals working in medical imaging segmentation often encounter challenges such as dealing with low-quality or noisy imaging data, anatomical variability among patients, and the need for precise delineation of structures. These challenges can be addressed by developing strong skills in image preprocessing, leveraging advanced segmentation algorithms (such as deep learning methods), and collaborating closely with radiologists or clinicians to validate results. Regular communication with multidisciplinary teams and staying updated with the latest research also help in overcoming these obstacles and improving segmentation accuracy.
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Medical Imaging Segmentation jobs near you
Infographic showing various Medical Imaging Segmentation job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 15% Part Time, and 6% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution.

Senior Consultant, AI/ML & Medical Imaging

People Force Consulting Inc

Foster City, CA • On-site

$106K - $145K/yr

Other

Posted 12 days ago

Job description

Job Description (JD) Senior Consultant, AI/ML & Medical Imaging

Location - Foster City, CA (Remote Option avaialble for a strong consultant)

Tentative duration - 6 - 12 months

Job Summary

We are seeking an experienced Senior Consultant AI/ML, Medical Imaging to lead the design, development, and delivery of advanced AI-powered medical imaging solutions. The role involves developing and optimizing deep learning models for image segmentation, feature extraction, radiomics, and risk prediction applications. The candidate will provide technical leadership across the project lifecycle, from solution architecture and model development to validation, deployment, and stakeholder engagement.

The ideal candidate should have strong expertise in medical image analysis, tumor segmentation using frameworks such as nnU-Net and MONAI, machine learning model development, and healthcare imaging standards.

Key Responsibilities

AI/ML Solution Development

  • Lead the design and implementation of AI/ML solutions for medical imaging and clinical research use cases.
  • Select, fine-tune, and optimize deep learning architectures for medical image segmentation and classification.
  • Develop tumor segmentation models using frameworks such as nnU-Net, MONAI, PyTorch, and related AI toolkits.
  • Drive model performance optimization through hyperparameter tuning, transfer learning, and data augmentation strategies.
  • Design and implement feature extraction pipelines, including radiomics and deep learning-based features.

Predictive Analytics & Risk Modeling

  • Develop risk prediction and prognostic models using imaging, clinical, and biomarker data.
  • Collaborate with clinical and research teams to identify relevant predictive features and outcomes.
  • Apply machine learning techniques for disease progression prediction, treatment response assessment, and patient stratification.

Model Evaluation & Validation

  • Define model evaluation frameworks and validation methodologies.
  • Establish performance metrics such as Dice Score, IoU, ROC-AUC, sensitivity, specificity, precision, recall, and calibration measures.
  • Ensure model robustness, reproducibility, explainability, and regulatory readiness.
  • Conduct statistical analysis and benchmark model performance against clinical requirements.

Technical Leadership

  • Provide overall technical delivery oversight for AI/ML imaging projects.
  • Mentor data scientists, AI engineers, and imaging specialists.
  • Review solution architecture, model design, and implementation approaches.
  • Manage technical risks, dependencies, and quality standards across project teams.

Stakeholder Collaboration

  • Collaborate with radiologists, pathologists, clinicians, research scientists, and product teams.
  • Translate clinical and business requirements into AI/ML solution designs.
  • Present technical findings, model performance results, and recommendations to leadership and clients.

Governance & Compliance

  • Ensure compliance with healthcare data standards and regulations.
  • Support AI model documentation, audit readiness, validation reports, and regulatory submissions where required.
  • Promote MLOps best practices for model lifecycle management, monitoring, and deployment.

Required Skills

Technical Skills

  • Strong expertise in Medical Imaging AI/ML.
  • Hands-on experience with nnU-Net, MONAI, and PyTorch.
  • Deep understanding of image segmentation, classification, object detection, and feature extraction techniques.
  • Experience with radiomics and multimodal imaging analytics.
  • Expertise in machine learning and deep learning algorithms.
  • Strong Python programming skills.
  • Experience with model evaluation and validation methodologies.
  • Knowledge of MLOps, model deployment, and monitoring practices.

Medical Imaging Knowledge

  • Understanding of CT, MRI, PET, Ultrasound, and Digital Pathology imaging modalities. Lung and breast imaging and
  • Familiarity with DICOM and healthcare imaging workflows.
  • Knowledge of oncology imaging and tumor segmentation methodologies.
  • Understanding of clinical trials, biomarkers, and imaging endpoints is preferred.

Data Science & Analytics

  • Statistical modeling and predictive analytics.
  • Risk prediction and survival analysis techniques.
  • Feature engineering and data preprocessing.
  • Explainable AI (XAI) approaches.

Required Experience

  • 10+ years of overall experience in AI/ML, data science, or medical imaging analytics.
  • 5+ years of hands-on experience in healthcare or life sciences AI applications.
  • Proven experience developing and deploying medical image segmentation solutions.
  • Experience leading technical delivery teams and client-facing engagements.
  • Demonstrated success in tumor segmentation, radiomics, and predictive modeling projects.

Preferred Qualifications

  • Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Biomedical Engineering, Medical Imaging, or related field.
  • Publications, patents, or research contributions in medical imaging AI.
  • Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform.
  • Experience with FDA, MDR, GxP, or healthcare regulatory environments.

Preferred Certifications

  • Microsoft Certified: Azure AI Engineer Associate
  • AWS Machine Learning Specialty
  • Google Professional Machine Learning Engineer
  • Medical Imaging AI or Healthcare AI-related certifications

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