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Medical Imaging Machine Learning Faculty Jobs (NOW HIRING)

NCSL International

Familiarity with machine-learning-based reconstruction or inference methods, including ... medical condition, genetic information, pregnancy, family structure, marital status, ancestry ...

Intuitive

Sr. Machine Learning Engineer

Sunnyvale, CA · On-site

$143K - $189K/yr

Drive the full cycle of medical imaging analysis software development, developing algorithms from R&D concepts into robust, commercial medical device products. * Design, prototype, and implement ...

Intuitive

Sr. Machine Learning Engineer

Sunnyvale, CA · On-site

$143K - $189K/yr

Drive the full cycle of medical imaging analysis software development, developing algorithms from R&D concepts into robust, commercial medical device products. * Design, prototype, and implement ...

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Medical Imaging Machine Learning Faculty information

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How much do medical imaging machine learning faculty jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for medical imaging machine learning faculty in the United States is $32.41, according to ZipRecruiter salary data. Most workers in this role earn between $18.27 and $39.42 per hour, depending on experience, location, and employer.

What is a medical imaging machine learning faculty?

Medical Imaging Machine Learning Faculty are academic professionals who teach, conduct research, and contribute to the advancement of machine learning techniques applied to medical imaging. They typically work in universities or research institutions, mentoring students and collaborating on interdisciplinary projects involving radiology, computer science, and healthcare. Their responsibilities include publishing research, securing grants, and developing innovative machine learning algorithms to improve diagnosis and treatment using medical images. These faculty members play a critical role in bridging the gap between artificial intelligence and clinical practice.

What are the key skills and qualifications needed to thrive as a medical imaging machine learning faculty?

To thrive as a Medical Imaging Machine Learning Faculty, you need a strong background in machine learning, computer vision, medical imaging, and a doctoral degree in a relevant field such as computer science, biomedical engineering, or a related discipline. Expertise with programming languages (like Python), deep learning frameworks (such as TensorFlow or PyTorch), and experience with medical imaging datasets and analysis tools are typically required. Excellent communication, collaboration, and mentoring abilities are crucial for teaching, research, and fostering interdisciplinary partnerships. These skills ensure effective research output, high-quality instruction, and innovation in advancing medical imaging technologies.

What are some common challenges faced by medical imaging machine learning faculty in balancing research, teaching, and clinical collaboration responsibilities?

Medical Imaging Machine Learning Faculty often juggle multiple responsibilities, including leading research projects, teaching graduate or undergraduate courses, and collaborating with clinical teams. A common challenge is effectively managing time between these areas, as each requires significant dedication and expertise. Balancing the fast-paced advancements in machine learning with the rigorous demands of academic publishing and curriculum development can also be demanding. Success in this role often depends on strong organizational skills, effective communication with multidisciplinary teams, and the ability to prioritize tasks to meet both academic and clinical goals.

What is the difference between Medical Imaging Machine Learning Faculty vs Medical Imaging Technologist?

AspectMedical Imaging Machine Learning FacultyMedical Imaging Technologist
CredentialsAdvanced degrees (PhD, Master's) in machine learning, medical imaging, or related fieldsAssociate's or Bachelor's degree in radiologic technology or related field
Work EnvironmentAcademic institutions, research labs, universitiesHospitals, clinics, imaging centers
Industry UsageResearch, teaching, development of new imaging algorithmsPerforming imaging procedures, patient positioning, image acquisition
Search & Comparison IntentResearch, academic careers, specialized roles in medical imaging AIClinical practice, patient care, imaging operations

Medical Imaging Machine Learning Faculty focus on research, teaching, and developing AI algorithms in medical imaging within academic or research settings. In contrast, Medical Imaging Technologists perform hands-on imaging procedures in clinical environments. While both roles involve medical imaging, their credentials, work environments, and job functions differ significantly.

What are popular job titles related to Medical Imaging Machine Learning Faculty jobs?

For Medical Imaging Machine Learning Faculty jobs, the most frequently searched job titles are:

Infographic showing various Medical Imaging Machine Learning Faculty job openings in the United States as of September 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $67,420 per year, or $32.4 per hour.

Project Scientist - Slomka Lab - Innovation in Imaging / Department of Medicine / Division of AIM

Los Angeles, CA • On-site

Cedars Sinai
Hospitals • 10K+ employees
Learn more about Cedars Sinaiexternal

Full-time

Re-posted 11 days ago

Cedars-Sinai rating

8.6

Company rating: 8.6 out of 10

Based on 131 frontline employees who took The Breakroom Quiz

44th of 1,066 rated hospitals

Job description

Are you ready to be a part of breakthrough research? Take action now by applying to this great opportunity!

Cedars-Sinai offers scientists unparalleled access to ground breaking technologies, facilities, and resources for conducting research related to an impressive range of diseases and disorders, from cancer, cardiology, and digestive diseases, to genetics, genomics, neurosciences, and women's health. You can continue your career journey with us!

The Slomka Laboratory focuses on developing innovative methods for fully automated analysis of nuclear cardiology data using novel algorithms and machine learning techniques, and on the development of integrated motion-corrected analysis of positron emission tomography (PET)/computed tomography (CT) angiography imaging.

As a Project Scientist, you will work independently and in cooperation with Piotr Slomka, PhD, Director, Innovation in Imaging and Principal Investigator, as part of a medical imaging and artificial intelligence research team within the Department of Medicine, Division of Artificial Intelligence. The incumbent will make significant and creative contributions to NIH research projects. This position will lead current and future research projects related to PET image reconstruction. Highly specialized and technical expertise in scientific programming, medical imaging and machine learning methods is required to carry out designated research projects. The Project Scientist is a key member of the research team and is expected to collaborate effectively with faculty investigators, engineers and research staff to achieve project objectives.

Primary Duties and Responsibilities:

  • Develops and tests new image processing and software methods.
  • Develops and implements new research techniques, protocols and algorithms, and data analysis and interpretation including image reconstruction and deep learning techniques.
  • Participates in publications, scientific abstracts, and presentations as first author or co-author.
  • Presents research results at scientific meetings.
  • Participates in research and technical meetings and applies expertise to plan and develop new research projects and proposals.
  • Assists in the preparation of grant proposals of Senior Investigators.
  • Compiles, analyzes, interprets, and summarizes data.
  • May submit grant proposals independently, as approved by supervising Principal Investigator.
  • May lead or train Staff Research Associates and Research Fellows.
Cedars-Sinai is a leader in providing high-quality healthcare encompassing primary care, specialized medicine and research. Since 1902, Cedars-Sinai has evolved to meet the needs of one of the most diverse regions in the nation, setting standards in quality and innovative patient care, research, teaching and community service. Today, Cedars- Sinai is known for its national leadership in transforming healthcare for the benefit of patients. Cedars-Sinai impacts the future of healthcare by developing new approaches to treatment and educating tomorrow's health professionals. Additionally, Cedars-Sinai demonstrates a commitment to the community through programs that improve the health of its most vulnerable residents.Cedars-Sinai is one of the largest nonprofit academic medical centers in the U.S., with 915 licensed beds, 2,993 physicians, 4,700+ nurses and thousands of other healthcare professionals and staff. Choose this if you want to work in a fast-paced environment that offers the highest level of care to people in the Los Angeles that need our care the most.

Education Requirements:

  • PhD in Computer Science or Biomedical Engineering or related field.

Experience and Skills:

  • Completion of postdoctoral scholar training.
  • Demonstrated significant, original or creative contributions to a research project or program.
  • Demonstrated scientific productivity in publications in peer-reviewed journals and/or grant submissions.
  • Knowledge of PI’s area of research specialization.
  • Experience in medical imaging, image processing, image reconstruction, machine learning and/or computer vision.
  • Proficiency in mathematics, medical statistics, and 3D geometry.
  • Proficiency in scientific and numerical C++ or Python programming.
  • Basic system administration skills.
  • Strong analytical and problem-solving skills.
  • Excellent communication (written and verbal) skills.

Physical Demands:

Able to perform moderate lifting.  Able to sit, stand and walk for prolonged periods of time.  Able to read papers and online documents.  Able to operate standard office equipment.  Able to exercise physical ability and perspective acuity to satisfactorily perform essential job functions.

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