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Machine Learning Winter Internship Jobs in Texas

Capital One

Lead Machine Learning Engineer

Plano, TX

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Lead Machine Learning Engineer (MLE), you'll be ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

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Machine Learning Winter Internship information

What types of projects and tasks can I expect to work on during a Machine Learning Winter Internship?

During a Machine Learning Winter Internship, you can expect to work on hands-on projects such as data preprocessing, building and evaluating machine learning models, and assisting with research experiments. Interns often contribute to real-world applications, such as developing predictive analytics tools, optimizing algorithms, or supporting deployment efforts. Collaboration is common, as you'll work closely with data scientists, engineers, and sometimes product teams, giving you exposure to the full machine learning workflow. This environment provides an excellent opportunity to apply theoretical knowledge, gain practical experience, and build a professional network in the field.

What is a Machine Learning Winter Internship?

A Machine Learning Winter Internship is a short-term, practical training program typically offered during the winter months for students or early-career professionals interested in machine learning. Interns work on real-world projects involving data analysis, model development, and algorithm implementation under the guidance of experienced mentors. The internship provides hands-on experience with tools and techniques used in the field, helping participants build technical skills and gain industry exposure. These positions are often offered by tech companies, research labs, or startups and can be either remote or onsite. Successful completion of a machine learning internship can enhance a candidate's resume and open up further career opportunities in artificial intelligence and data science.

What are the key skills and qualifications needed to thrive as a Machine Learning Winter Intern, and why are they important?

To thrive as a Machine Learning Winter Intern, you generally need a solid foundation in mathematics, programming (especially Python), and a basic understanding of machine learning concepts, often acquired through coursework or relevant projects. Familiarity with tools such as TensorFlow, PyTorch, and data analysis libraries like Pandas and NumPy is typically required. Strong problem-solving abilities, collaboration, and curiosity help interns stand out in team-based, fast-paced environments. These skills are crucial for effectively contributing to real-world projects and quickly learning from experienced professionals during the internship.

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

AspectMachine Learning Winter InternshipData Science Winter Internship
Required CredentialsUndergraduate or graduate in CS, AI, or related fields; some knowledge of ML frameworksUndergraduate or graduate in Statistics, Math, CS; familiarity with data analysis tools
Work EnvironmentResearch labs, tech companies, startups focusing on ML modelsData analysis, visualization, and interpretation in various industries
Employer & Industry UsageTech firms, AI startups, research institutionsTech companies, finance, healthcare, consulting

While both internships involve working with data, the Machine Learning Winter Internship emphasizes developing and applying ML algorithms, whereas the Data Science Winter Internship focuses on analyzing data, creating reports, and deriving insights. Candidates should choose based on their specific skills and career goals in AI or data analysis.

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Machine Learning Winter Internship jobs near you
Infographic showing various Machine Learning Winter Internship job openings in Texas as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Data Science Machine Learning Internship (Summer 2027)

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International LLC

Houston, TX

Full-time

Posted 2 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