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Medical Machine Learning Internship Jobs (NOW HIRING)

Jane Street

Machine Learning Engineer

New York, NY · On-site

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 ...

Jane Street

Machine Learning Engineer

New York, NY

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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Medical Machine Learning Internship information

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$25.5K

$42.6K

$88K

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

As of Aug 4, 2026, the average yearly pay for medical machine learning internship 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 kinds of projects and responsibilities can I expect during a medical machine learning internship?

As a Medical Machine Learning Intern, you'll typically work on real-world datasets to develop, test, and refine machine learning models aimed at improving healthcare outcomes. Your daily tasks may include data preprocessing, feature engineering, model selection, and performance evaluation under the guidance of experienced data scientists or clinicians. Collaboration is key, as you'll often work with multidisciplinary teams, including software engineers, clinicians, and researchers. This hands-on experience allows you to gain practical skills and contribute to impactful projects that can enhance patient care or medical research.

What are the key skills and qualifications needed to thrive as a medical machine learning intern, and why are they important?

To thrive as a Medical Machine Learning Intern, you need a solid background in statistics, programming (Python or R), and foundational knowledge of machine learning algorithms, typically supported by coursework or a relevant degree in computer science, engineering, or a related field. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience working with medical datasets or healthcare IT systems are highly valued. Strong analytical thinking, attention to detail, and effective communication skills will help you collaborate with interdisciplinary teams and present findings clearly. These skills are crucial for developing accurate, impactful models that address real-world healthcare challenges and ensure patient safety.

What is a medical machine learning internship?

A Medical Machine Learning Internship is a temporary position where students or recent graduates work on projects that apply machine learning techniques to healthcare and medical datasets. Interns typically collaborate with data scientists, clinicians, and researchers to develop algorithms that can assist in diagnosing diseases, predicting patient outcomes, or improving healthcare processes. This role provides hands-on experience with real-world medical data, exposure to regulatory considerations, and the opportunity to contribute to impactful healthcare innovations. Such internships help interns build technical and domain-specific skills, preparing them for future careers in medical data science.
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Medical Machine Learning Internship jobs near you
Infographic showing various Medical Machine Learning Internship job openings in the United States as of July 2026, with employment types broken down into 3% Internship, 71% Full Time, 24% Part Time, 1% Temporary, and 1% Contract. Highlights an 83% Physical, 1% Hybrid, and 16% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International, LLC

Stamford, CT • On-site

Full-time

Posted 15 days ago

Job description

Application Deadline: September 1, 11:59pm EST
Program Summary - Commercial Technology Internships
Company Overview:
Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets, providing the Company with market insights and access. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in, and develop, select commodity infrastructure assets. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.
Position Overview:
CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are looking for motivated and detail-oriented Machine Learning Interns with a strong interest in quantitative analysis, particularly time series forecasting to join our Global Data Science team in Stamford, CT, Houston, TX, or New York City offices. Our Machine Learning Internship provides a unique opportunity to work with fundamental market data, generating insights that support our commercial trading business. You will be responsible for analyzing time series data related to market fundamentals in the Power, Natural Gas, and Oil sectors, helping to identify key supply and demand drivers. These insights will play a vital role in forecasting price movements and supporting risk management decisions.
Responsibilities:
  • Apply mathematical and statistical knowledge to enhance existing machine learning applications and explore new solutions.
  • Work closely with Data Scientists, Analysts, and Traders to design, implement, and optimize machine learning models for time series forecasting, including ARIMA/SARIMA, gradient boosting methods (e.g., XGBoost), LSTM networks, and linear regression-based approaches.
  • Assist in designing and implementing end-to-end data ingestion processes, ensuring seamless data flow to investing teams.
  • Work with desk heads, traders, and analysts to understand current data architecture, investment processes, and functional requirements for data science analysis.
  • Contribute to identifying and back-testing new data sets, leveraging machine learning techniques to drive insights.
  • Conduct ad hoc research on emerging project topics, including energy fundamental data, analytics trends, and best practices in big data and artificial intelligence.

Qualifications:
  • Currently pursuing a Bachelor's Degree or higher in Mathematics, Statistics, Physics, Computer Science or related technical field with a focus in Machine Learning.
  • Expected graduation date of Winter 2027 or Spring/Summer 2028.
  • Experience applying machine learning techniques such as regression, time series forecasting, deep learning, reinforcement learning, or predictive modeling to solve problems involving complex data patterns and market dynamics.
  • Strong programming experience in Python (preferred libraries: Pandas, NumPy, etc.)
  • Ability to communicate and interact with a wide range of users, from very technical to non-technical backgrounds.
  • Strong analytical skills with demonstrated attention to detail.

Apply