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Machine Learning Internship Microsoft 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 ...

Tech Consulting

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Machine Learning Engineer

Dallas, TX · On-site

$30 - $40/hr

In this role, you will assist in designing, developing, and deploying machine learning models and ... Internship, academic project, or personal project in AI/ML. * Knowledge of deep learning and neural ...

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Machine Learning Internship Microsoft information

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$25.5K

$42.6K

$88K

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

As of Jul 29, 2026, the average yearly pay for machine learning internship microsoft 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 Internship at Microsoft?

A Machine Learning Internship at Microsoft is a temporary position for students or recent graduates to gain hands-on experience working on real-world machine learning projects. Interns collaborate with experienced engineers and researchers to develop, test, and deploy machine learning models and solutions that impact Microsoft products and services. The internship typically involves working with large datasets, implementing algorithms, and contributing to team goals while learning about cutting-edge AI technologies. Interns also benefit from mentorship, networking opportunities, and exposure to the latest industry practices.

What types of projects do interns typically work on during a Machine Learning Internship at Microsoft?

As a Machine Learning intern at Microsoft, you can expect to work on impactful, real-world projects that contribute to ongoing products or research initiatives. Interns often collaborate with data scientists, software engineers, and product teams to develop, test, and refine machine learning models for applications such as natural language processing, computer vision, or recommendation systems. You'll likely participate in code reviews, present your findings, and receive mentorship from experienced professionals, all within a collaborative and innovative environment. These projects not only enhance technical skills but also provide valuable exposure to large-scale, industry-leading systems.

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

AspectMachine Learning Internship MicrosoftData Science Internship Microsoft
Required SkillsProgramming, ML algorithms, Python, TensorFlowStatistics, data analysis, Python, SQL
Work EnvironmentResearch and development teams focused on ML modelsData analysis and visualization teams
Industry UsageAI and ML product developmentBusiness insights and data-driven decision making

Both internships are highly competitive roles at Microsoft, often requiring programming skills and relevant coursework. Machine Learning Internships focus on developing and deploying ML models, while Data Science Internships emphasize analyzing data to generate insights. Candidates should review the specific role descriptions to align their skills accordingly.

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

To thrive as a Machine Learning Intern at Microsoft, you need a solid foundation in mathematics, programming (especially Python), and machine learning concepts, typically supported by coursework or related projects. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and experience using cloud platforms like Azure are often expected. Strong problem-solving skills, curiosity, and effective communication help you collaborate with team members and present findings. These skills are crucial for contributing to innovative projects and translating complex data-driven insights into impactful solutions within a dynamic tech environment.
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Machine Learning Internship Microsoft jobs near you
Infographic showing various Machine Learning Internship Microsoft job openings in the United States as of July 2026, with employment types broken down into 3% Internship, 73% Full Time, 22% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% 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

Full-time

Posted 8 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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