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Machine Learning Summer Internship Jobs in New York

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 ...

Hang

Machine Learning Engineer

New York, NY · On-site +1

Hang raised a $16 million Series A led by Paradigm last summer, with participation from Tiger ... This person will implement and develop machine learning models to enhance our platform ...

Jane Street

Machine Learning Researcher

New York, NY · On-site

You'll spend the bulk of your internship working closely with full-time machine learning researchers on projects drawn from their own work. You might conduct an end-to-end study of an unexplored ...

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Machine Learning Summer Internship information

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

To thrive as a Machine Learning Summer Intern, you need a solid background in mathematics, statistics, and programming (especially Python), often supported by ongoing coursework in computer science or related fields. Familiarity with machine learning frameworks like TensorFlow or PyTorch, version control systems such as Git, and data analysis tools is typically required. Strong problem-solving skills, curiosity, and teamwork are important soft skills that help interns contribute effectively and learn quickly. These skills and qualities are crucial for applying theoretical knowledge, collaborating on real projects, and adapting to the fast-evolving field of machine learning.

What types of projects can I expect to work on during a machine learning summer internship?

As a Machine Learning Summer Intern, you can expect to contribute to projects such as data preprocessing, building and evaluating machine learning models, and assisting with the deployment of algorithms into production environments. Interns often work alongside data scientists and engineers on real-world datasets to solve business problems, develop prototypes, or improve existing models. This hands-on experience will help you gain practical skills in using popular ML frameworks and understanding the end-to-end machine learning workflow.

What is the difference between Machine Learning Summer Internship vs Data Science Summer Internship?

AspectMachine Learning Summer InternshipData Science Summer Internship
Required CredentialsBasic programming, math, and machine learning knowledgeProgramming, statistics, and data analysis skills
Work EnvironmentDeveloping ML models, algorithms, and prototypesData analysis, visualization, and reporting
Industry UsageTech companies, AI startups, research labsBusiness, finance, healthcare, tech firms

Both internships involve working with data and require programming skills, but Machine Learning Summer Internships focus on developing algorithms and models, while Data Science Summer Internships emphasize data analysis and insights. The choice depends on your interest in building models versus analyzing data.

What is a machine learning summer internship?

A Machine Learning Summer Internship is a temporary, typically 8-12 week program for students or recent graduates to gain practical experience in machine learning. Interns work under the supervision of experienced professionals, contributing to real-world projects involving data analysis, model development, and algorithm implementation. These internships often provide mentorship, networking opportunities, and exposure to the latest tools and technologies in the field. They are valuable for building technical skills and improving career prospects in artificial intelligence and data science.
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What job categories do people searching Machine Learning Summer Internship jobs in New York look for? The top searched job categories for Machine Learning Summer Internship jobs in New York are:
What cities in New York are hiring for Machine Learning Summer Internship jobs? Cities in New York with the most Machine Learning Summer Internship job openings:
Machine Learning Summer Internship jobs near you
Infographic showing various Machine Learning Summer Internship job openings in New York as of July 2026, with employment types broken down into 1% As Needed, 70% Full Time, 23% Part Time, 3% Temporary, and 3% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International, LLC

Stamford, CT • On-site

Full-time

Posted 17 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.

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