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Internship Machine Learning Finance Jobs (NOW HIRING)

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Machine Learning Intern

Mountain View, CA · Hybrid

$40/hr

This internship will pay $40 per hour, with an expected 40 hours per week for the 12-week program ... of machine learning and deep learning algorithms. Familiarity with training or fine-tuning large ...

Aivra Health LLC

$28 - $45/hr

Machine Learning Engineer Intern United States Internship | Full-Time (40 hours/week) Pay Range: $28 - $45 per hour Visa: H1B Sponsorship Available | STEM OPT, OPT & CPT Candidates Welcome Position ...

Aivra Health LLC

$28 - $45/hr

Machine Learning Engineer Intern United States Internship | Full-Time (40 hours/week) Pay Range: $28 - $45 per hour Visa: H1B Sponsorship Available | STEM OPT, OPT & CPT Candidates Welcome Position ...

Coupang

Machine Learning Intern

Mountain View, CA · On-site

$40 - $45/hr

What you will do In this internship, you will gain hands-on experience building large-scale machine learning models for Ads retrieval and ranking. Additionally, you will have the opportunity to ...

Instacart

OR

Overview We are looking for interns to join Instacart's Economics team. The ideal candidate for ... finance, or operations research. Candidates should bring some relevant research experience ...

Avride

Machine Learning Engineer Internship

Austin, TX · On-site

About the Internship At Avride, ML Engineer Interns operate at the intersection of cutting-edge ... Machine Learning / Math Foundation: Strong understanding of deep learning, reinforcement learning ...

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How much do internship machine learning finance jobs pay per hour?

As of May 29, 2026, the average hourly pay for internship machine learning finance in the United States is $19.86, according to ZipRecruiter salary data. Most workers in this role earn between $17.07 and $22.36 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Intern in Machine Learning Finance, and why are they important?

To thrive as an Intern in Machine Learning Finance, you need a foundational understanding of statistics, programming (especially Python or R), and financial concepts, often supported by progress toward a quantitative degree. Familiarity with machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), data analysis tools, and version control systems like Git is typically expected. Strong analytical thinking, problem-solving abilities, and effective communication help you translate technical results into actionable financial insights. These skills are critical for developing robust models, supporting data-driven decision-making, and contributing meaningfully within interdisciplinary finance teams.

What types of projects do interns typically work on in a Machine Learning Finance internship?

As a Machine Learning Finance intern, you can expect to work on a variety of projects that blend quantitative analysis with practical financial applications. Common responsibilities include developing predictive models for stock prices or credit risk, analyzing large financial datasets, and building tools to automate trading strategies or detect fraud. Interns often collaborate closely with data scientists, software engineers, and finance professionals, gaining exposure to both technical and business aspects of the field. This hands-on experience is invaluable for building real-world skills and understanding the fast-paced finance environment.

What are Internship Machine Learning Finance positions?

Internship Machine Learning Finance positions are temporary roles where students or recent graduates work with financial organizations to apply machine learning techniques to solve finance-related problems. Interns may analyze large datasets, build predictive models, automate trading strategies, or detect fraud using machine learning algorithms. These internships provide hands-on experience in both finance and artificial intelligence, helping interns develop technical and industry-specific skills. They often require a background in programming, statistics, and a basic understanding of financial concepts.

What is the difference between Internship Machine Learning Finance vs Data Analyst Intern?

AspectInternship Machine Learning FinanceData Analyst Intern
Required SkillsProgramming (Python, R), Machine Learning, Finance knowledgeData analysis, SQL, Excel, basic statistics
Work EnvironmentFinance firms, tech-driven finance teamsFinancial institutions, consulting firms, tech companies
Industry UsageFinance, Fintech, Quantitative researchFinance, marketing, consulting

Internship Machine Learning Finance focuses on applying machine learning techniques to financial data, requiring programming and finance knowledge. Data Analyst Internships involve analyzing data sets, creating reports, and using statistical tools. Both roles are common in finance-related industries but differ in technical focus and skill requirements.

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What cities are hiring for Internship Machine Learning Finance jobs? Cities with the most Internship Machine Learning Finance job openings:
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What job categories do people searching Internship Machine Learning Finance jobs look for? The top searched job categories for Internship Machine Learning Finance jobs are:
Internship Machine Learning Finance jobs near you
Infographic showing various Internship Machine Learning Finance job openings in the United States as of May 2026, with employment types broken down into 14% Full Time, 83% Part Time, and 3% Contract. Highlights an 66% Physical, 17% Hybrid, and 17% Remote job distribution, with an average salary of $41,299 per year, or $19.9 per hour.
Machine Learning Internship - PhD: 2027

Machine Learning Internship - PhD: 2027

Susquehanna International Group, LLP

Philadelphia, PA • On-site

Full-time, Internship

Posted 20 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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