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Internship Medical Imaging Machine Learning Jobs in Boston, MA

Bracebridge Capital

Machine Learning Analyst

Boston, MA · On-site

$110K - $145K/yr

We are seeking a Machine Learning Analyst to join the growing data analytics and machine learning ... internships, undergraduate research or thesis, or substantial independent technical projects ...

Bracebridge Capital

Machine Learning Analyst

Boston, MA · On-site

$110K - $145K/yr

We are seeking a Machine Learning Analyst to join the growing data analytics and machine learning ... internships, undergraduate research or thesis, or substantial independent technical projects ...

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Internship Medical Imaging Machine Learning information

See Boston, MA salary details

$27.7K

$46.3K

$95.6K

How much do internship medical imaging machine learning jobs pay per year?

As of Sep 12, 2026, the average yearly pay for internship medical imaging machine learning in Boston, MA is $46,263.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,300.00 and $50,000.00 per year, depending on experience, location, and employer.

What is an internship in medical imaging machine learning?

An Internship in Medical Imaging Machine Learning is a temporary position, typically held by students or recent graduates, where individuals gain practical experience applying machine learning techniques to medical imaging data. Interns work alongside professionals to develop, train, and evaluate algorithms that help interpret medical images such as X-rays, MRIs, or CT scans. These roles often involve data preprocessing, model development, and performance analysis, providing valuable hands-on experience in both healthcare and artificial intelligence. This internship is ideal for those interested in combining expertise in computer science, machine learning, and medical diagnostics.

What types of projects can I expect to work on during an internship in medical imaging machine learning?

As an intern in medical imaging machine learning, you'll typically contribute to projects involving the development and validation of algorithms for tasks such as image segmentation, classification, or anomaly detection using clinical imaging data. Daily responsibilities often include data preprocessing, model training and evaluation, and collaborating with research scientists and clinicians to interpret results. You'll also have opportunities to participate in team meetings, present findings, and receive mentorship, which can help you build both technical and domain-specific skills for future roles in healthcare AI.

What are the key skills and qualifications needed to thrive as an intern in medical imaging machine learning?

To thrive in a Medical Imaging Machine Learning internship, you need a solid background in computer science, mathematics, and biomedical engineering, typically supported by coursework or experience in machine learning and image processing. Familiarity with Python, TensorFlow or PyTorch, and medical imaging tools such as DICOM viewers is commonly required. Strong analytical thinking, problem-solving abilities, and effective teamwork set candidates apart in this field. These skills and qualities enable interns to contribute meaningfully to research and development projects, ensuring accurate analysis and innovation in medical imaging solutions.

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Internship Medical Imaging Machine Learning jobs near you
Infographic showing various Internship Medical Imaging Machine Learning job openings in Boston, MA as of September 2026, with employment types broken down into 1% As Needed, 77% Full Time, 15% Part Time, and 7% Contract. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution, with an average salary of $46,263 per year, or $22.2 per hour.

Staff Machine Learning Engineer

Boston, MA • On-site

AVID Technical Resources
IT Services • 201 - 500 employees
Learn more about AVID Technical Resourcesexternal

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Overview
We are partnering with a fast-growing healthcare technology company at the forefront of AI-driven medical innovation. With strong clinical validation, regulatory progress, and real-world adoption, the organization is advancing how medical imaging is used to predict disease risk and enable earlier, more proactive care.
This is a high-impact, senior technical role focused on developing next-generation machine learning models for medical imaging. You will play a key role in shaping the company's ML strategy, advancing core algorithms, and setting best practices for model development in a regulated environment.
What You'll Do
  • Lead the research and development of state-of-the-art deep learning models for medical imaging applications
  • Design and implement scalable training pipelines for large-scale imaging datasets
  • Drive model performance through experimentation, architecture innovation, and rigorous evaluation frameworks
  • Translate clinical and scientific requirements into production-ready ML solutions
  • Collaborate cross-functionally with data scientists, engineers, and clinical stakeholders
  • Mentor and provide technical leadership to ML engineers and data scientists
  • Partner with infrastructure teams to optimize GPU utilization, training efficiency, and deployment workflows
  • Contribute to scientific publications and support intellectual property development
What You Bring
Required
  • PhD with 4+ years of industry experience, or Master's with 7+ years in Machine Learning, AI, or a related field
  • Deep expertise in machine learning and self-supervised learning methods
  • Strong programming skills in Python and PyTorch
  • Experience building and scaling ML systems in cloud environments (AWS preferred)
  • Hands-on experience with medical imaging data (DICOM) and preprocessing pipelines
  • Proven track record of developing and deploying ML models in healthcare, biotech, or other regulated industries
  • Strong communication skills and ability to collaborate across technical and clinical teams
Preferred
  • Experience with mammography or breast imaging
  • Familiarity with FDA regulatory processes for Software as a Medical Device (SaMD)
  • Experience with ML orchestration tools (e.g., Metaflow or similar)
  • Expertise in distributed training (multi-GPU/multi-node) and performance optimization
  • Publications in leading ML or medical imaging conferences (e.g., NeurIPS, ICML, MICCAI)
  • Experience with foundation models, transfer learning, or domain adaptation in medical imaging

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