ZipRecruiter

Log In

1

Machine Learning Unpaid Internship Jobs (NOW HIRING)

Odyssey House Inc.

Unpaid Intern

Salt Lake City, UT · On-site

$14.50 - $19.25/hr

Learning Objectives Interns will gain exposure to: * Real-world application of academic concepts in ... This is an unpaid internship * No wages, stipends, or employment benefits are provided * Internship ...

previous pagenext page

Showing results 21-40

All JobsMachine Learning Unpaid JobsMachine Learning Unpaid Internship Jobs

Machine Learning Unpaid Internship information

See salary details

$25.5K

$42.6K

$88K

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

As of Aug 16, 2026, the average yearly pay for machine learning unpaid 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 is a machine learning unpaid internship?

A Machine Learning Unpaid Internship is a temporary position where students or recent graduates work with professionals to gain practical experience in machine learning without receiving monetary compensation. Interns typically assist with data analysis, model development, and research tasks, while learning about real-world machine learning applications. These internships help individuals build relevant skills, expand their professional network, and improve their resumes for future job opportunities.

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

AspectMachine Learning Unpaid InternshipData Science Intern
Required CredentialsBasic programming, coursework in ML or AIStatistics, programming, data analysis skills
Work EnvironmentStartups, tech companies, research labsTech firms, consulting agencies, research institutions
Industry UsageFocus on ML model development and algorithmsBroader data analysis, visualization, reporting

The main difference is that a Machine Learning Unpaid Internship emphasizes developing ML models and algorithms, often requiring knowledge of programming and AI concepts. In contrast, a Data Science Intern role covers a wider range of data analysis tasks, including visualization and reporting. Both roles are common in tech and research environments, but the internship focus and skill requirements differ slightly.

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

To thrive as a Machine Learning Unpaid Intern, you should have a solid understanding of linear algebra, statistics, and programming languages like Python, along with coursework or experience in machine learning concepts. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is typically expected. Strong problem-solving skills, eagerness to learn, and effective communication set standout interns apart. These skills are essential for contributing to projects, adapting quickly in a dynamic field, and collaborating with team members on real-world machine learning challenges.

What kinds of projects and responsibilities can I expect during a machine learning unpaid internship?

As a machine learning unpaid intern, you will typically work on projects such as data preprocessing, model training, and performance evaluation under the guidance of experienced team members. Your daily tasks may include cleaning datasets, implementing algorithms, and conducting experiments to test model improvements. Interns often collaborate with data scientists and engineers, participating in team meetings and code reviews to learn best practices. This hands-on experience provides valuable exposure to real-world machine learning workflows and tools, helping you build skills that are essential for future roles in the field.
More about Machine Learning Unpaid Internship jobs

What cities are hiring for Machine Learning Unpaid Internship jobs?

Cities with the most Machine Learning Unpaid Internship job openings:

What states have the most Machine Learning Unpaid Internship jobs?

States with the most job openings for Machine Learning Unpaid Internship jobs include:

What job categories do people searching Machine Learning Unpaid Internship jobs look for?

The top searched job categories for Machine Learning Unpaid Internship jobs are:

Machine Learning Unpaid Internship jobs near you
Infographic showing various Machine Learning Unpaid Internship job openings in the United States as of August 2026, with employment types broken down into 33% Internship, 11% Full Time, and 56% Part Time. Highlights an 100% In-person 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

Houston, TX

Full-time

Posted 25 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