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Internship Deep Learning Jobs in New York (NOW HIRING)

Synaptrix Labs

Internship Program

New York, NY · On-site

$18.25 - $23.75/hr

... deep learning, signal processing, and computational neuroscience. We're seeking passionate ... Availability for an internship in New York City. Preferred Qualifications: * Prior experience with ...

Synaptrix Labs

Internship Program

New York, NY

$18.25 - $23.75/hr

... deep learning, signal processing, and computational neuroscience. We're seeking passionate ... Availability for an internship in New York City. Preferred Qualifications: * Prior experience with ...

Synaptrix Labs

Internship Program

New York, NY

$18.25 - $23.75/hr

... deep learning, signal processing, and computational neuroscience. We're seeking passionate ... Availability for an internship in New York City. Preferred Qualifications: * Prior experience with ...

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Internship Deep Learning information

What is an internship in deep learning?

An internship in deep learning is a temporary position, typically offered to students or recent graduates, where individuals gain practical experience working on projects involving neural networks, machine learning algorithms, and AI applications. Interns often assist with data preparation, model training, evaluation, and sometimes contribute to research or development of deep learning solutions. This role helps interns develop technical skills, gain exposure to real-world problems, and build a foundation for a career in artificial intelligence or related fields.

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

AspectInternship Deep LearningData Science Intern
Required SkillsMachine learning, neural networks, programming (Python, TensorFlow)Statistics, data analysis, programming (Python, R)
Work EnvironmentResearch-focused, AI/ML teams, tech companiesBusiness analytics, data analysis teams, various industries
Common Employer UsageTech firms, AI startups, research labsConsulting firms, tech companies, finance, healthcare

Internship Deep Learning roles focus on developing neural networks and AI models, often in research or tech environments. Data Science Internships involve analyzing data, creating insights, and supporting decision-making across diverse industries. Both internships require programming skills, but Deep Learning emphasizes AI-specific knowledge, while Data Science covers broader data analysis skills.

What types of projects or tasks can I expect to work on during a deep learning internship?

As a Deep Learning intern, you can typically expect to work on a variety of hands-on projects such as data preprocessing, model development, and performance evaluation. You may contribute to building and testing neural networks, experimenting with architectures like CNNs or RNNs, and assisting in preparing datasets for training. Collaboration with data scientists, engineers, and other interns is common, providing opportunities to learn best practices in model deployment and documentation. This role offers a valuable chance to gain practical experience in applying theoretical knowledge to real-world problems.

What are the key skills and qualifications needed to thrive in a deep learning internship, and why are they important?

To thrive in a Deep Learning Internship, you need a strong foundation in mathematics, programming (especially Python), and machine learning concepts, typically supported by ongoing or completed studies in computer science or a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch and experience using tools like Jupyter Notebook are highly valued. Strong problem-solving skills, curiosity, and effective communication help interns stand out when working on complex projects and collaborating with teams. These skills and qualities are crucial for efficiently developing, testing, and explaining deep learning models in a fast-evolving field.
What are the most commonly searched types of Deep Learning jobs in New York? The most popular types of Deep Learning jobs in New York are:
What are popular job titles related to Internship Deep Learning jobs in New York? For Internship Deep Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Internship Deep Learning jobs in New York look for? The top searched job categories for Internship Deep Learning jobs in New York are:
Internship Deep Learning jobs near you
Infographic showing various Internship Deep Learning job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Data Science Machine Learning Internship (Summer 2027)

Castleton Commodities International, LLC

Stamford, CT • On-site

Full-time

Posted 18 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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