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Machine Learning Unpaid Internship Jobs (NOW HIRING)

Susquehanna

Overview Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data ...

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 ...

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Machine Learning Unpaid Internship information

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$42.6K

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

As of Sep 5, 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 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.

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 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.

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What states have the most Machine Learning Unpaid Internship jobs?

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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 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Internship - PhD: 2027

Susquehanna

Bala Cynwyd, PA

Full-time, Internship

Posted 10 days ago

Job description

Overview

Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and markets.

As a Machine Learning Intern at Susquehanna, you'll work on high-impact projects that closely reflect the challenges and workflows of our full-time research team. You'll apply your technical expertise in machine learning and data science to real-world financial problems, while developing a deep understanding of how machine learning integrates into Susquehanna's research and trading systems. You will leverage vast and diverse datasets and apply cutting-edge machine learning at scale to drive data-informed decisions in predictive modeling to strategic execution.

What You Can Expect

  • Conduct research and develop ML models to identify patterns in noisy, non-stationary data
  • Work side-by-side with our Machine Learning team on real, impactful problems in quantitative trading and finance, bridging the gap between cutting-edge ML research and practical implementation
  • Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches
  • Design and run experiments using the latest ML tools and frameworks
  • One-on-one mentorship from experienced researchers and technologists
  • Participate in a comprehensive education program with deep dives into Susquehanna's ML, quant, and trading practices
  • Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior
  • Explore various aspects of machine learning in quantitative finance from alpha generation and signal processing to model deployment and risk-aware decision making

What we're looking for

  • Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field
  • Proven experience applying machine learning techniques in a professional or academic setting
  • Strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR
  • Hands-on experience with machine learning frameworks, including PyTorch and TensorFlow
  • Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment

Why Join Us?

  • Work with a world-class team of researchers and technologists
  • Access to unparalleled financial data and computing resources
  • Opportunity to make a direct impact on trading performance
  • Collaborative, intellectually stimulating environment with global reach

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.

If you're a recruiting agency and want to partner with us, please reach out to recruiting@sig.com. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.

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