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Internship Medical Imaging Machine Learning Jobs

As a Postdoctoral Associate in Diagnostic Imaging & Machine Learning , you will join an innovative research team dedicated to advancing next-generation medical imaging technologies. Your expertise in ...

As a Postdoctoral Associate in Diagnostic Imaging & Machine Learning , you will join an innovative research team dedicated to advancing next-generation medical imaging technologies. Your expertise in ...

Core experience developing machine learning models for biomedical applications, specifically in medical imaging, computational pathology, genomics, transcriptomics, multi-omics, or molecular ...

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

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Internship Medical Imaging Machine Learning information

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$25.5K

$42.6K

$88K

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

As of Aug 21, 2026, the average yearly pay for internship medical imaging machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 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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Infographic showing various Internship Medical Imaging Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 15% Part Time, and 7% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Imaging Machine Learning Engineer

Socket.dev

Mountain View, CA • On-site

$132 - $189/hr

Other

Posted 8 days ago


Job description

Minimum qualifications:
  • Bachelor’s degree in Electrical Engineering, Computer Science, Imaging Science, Physics, or a related field, or equivalent practical experience.
  • 2 years of experience in Image Quality, Computer Vision, or a related technical field.
  • 2 years of experience in Python and C++ for algorithm development and implementation.
Preferred qualifications:
  • Master’s degree, or PhD in a related field.
  • 3 years of professional experience in a related field.
  • Industry experience in SoC/ISP constraints, 3A tuning, or the application of Deep Learning to real-time imaging pipelines.
  • A proven track record of delivering high-quality, commercial, market-facing consumer cameras.
  • A strong track record of self-driven learning and the capability to rapidly master new technologies and domains.
  • Excellent written and verbal communication skills, with a demonstrated ability to translate technical concepts into clear, actionable insights for cross-functional partners.
About the job:

The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $132000 - $189000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:
  • Optimize image quality across the hardware and software stack, ensuring hardware capabilities are leveraged through software tuning.
  • Fine-tune 3A algorithms to ensure performance across lighting and environmental conditions.
  • Build software tools, utilizing machine learning (ML) automation to streamline image quality (IQ) tuning, testing, benchmarking, and calibration workflows.
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