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Internship Machine Learning Finance Jobs (NOW HIRING)

Quanta Search

Machine Learning Engineer

Manhattan, NY · On-site

Successful candidates will also have deep interest in learning about trading and the financial ... Machine Learning Engineers build production grade machine learning algorithms that operate in real ...

Coinflow

Machine Learning Engineer

Chicago, IL · On-site

$160K - $220K/yr

... global financial infrastructure with stablecoins, AI-driven fraud prevention, and instant ... Coinflow is seeking a Machine Learning Engineer to help build the intelligence layer that powers ...

Robinhood

Machine Learning Engineer

Bellevue, WA

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be ... We're looking for an exceptional Machine Learning Engineer to help shape the future of our core ...

Robinhood

Machine Learning Engineer

Bellevue, WA · On-site

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be ... We're looking for an exceptional Machine Learning Engineer to help shape the future of our core ...

ICONMA

Machine Learning Engineer

Mclean, VA

Machine Learning Engineer Our client, a financial company, is looking for a Machine Learning Engineer for their McLean, VA location. Requirements: * Python, AWS, Kubernetes, Kubeflow, MLOps, ML ...

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $160K/yr

Desired Qualifications * 0-4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing-or a strong recent graduate with demonstrated ...

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

As of Jun 18, 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 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 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 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 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.

More about Internship Machine Learning Finance jobs
What cities are hiring for Internship Machine Learning Finance jobs? Cities with the most Internship Machine Learning Finance job openings:
What are the most commonly searched types of Machine Learning Finance jobs? The most popular types of Machine Learning Finance jobs are:
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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 June 2026, with employment types broken down into 96% Full Time, 3% Part Time, and 1% Contract. Highlights an 85% Physical, 1% Hybrid, and 14% Remote job distribution, with an average salary of $41,299 per year, or $19.9 per hour.

Machine Learning Engineer

Quanta Search

Manhattan, NY • On-site

Full-time

Posted 28 days ago

Job description

Our client is a process driven investment management group consisting of a team of researchers, traders and technologists who harness and apply the power of technology and automation to identify, model and trade global financial markets. This division offers an array of quantitative investment fund products to its clients.
They are seeking candidates with exceptional academic credentials to join their team and participate in and support of the firm's efforts in the research, trading and production processes.
They look for candidates who are eager to make an impact by doing real, hands-on research and development. Candidates must possess exceptional knowledge of mathematical and statistical methods as well as a proven ability to solve complex problems. A desire to work with large data sets and apply creative thinking is required. Successful candidates will also have deep interest in learning about trading and the financial markets.
They offer a supportive environment that fosters independent thought in a collegial, results oriented, work setting. Researchers and developers there are passionate about their work, model building, data and technology.
You are curious and intellectually driven to succeed. You'll be provided with the tools, resources and training required to satisfy that curiosity and passion, leading them to new insights and discoveries. Their process driven approach enables these insights to be thoroughly tested in a systematic fashion and ultimately, if confirmed, integrated into the portfolio.
Role:
Machine Learning Engineers build production grade machine learning algorithms that operate in real time or at scale. They have a very deep understanding of machine learning algorithms and cloud computing. Machine Learning Engineers should be comfortable with data engineering and should have an interest in the data science.
What they will do:
They will be responsible for their production grade signal generation and ML systems. They can act as data scientists, but should be comfortable pushing their algorithms, models, and signals into production.
Minimum Requirements:
  • Strong understanding of statistical analysis and computational modelling.
  • Strong understanding of algorithms and data structures.
  • Familiar with map reduce and big data processing (Spark, Hadoop, DataFlow, etc).
  • TensorFlow (or another GPU integrated deep learning library).
  • Deep understanding of machine learning algorithms.
  • Deep understanding of numerical optimization.
  • Strong understanding of data structures and algorithms.

Plus, but not required:
  • Previous experience in tech industry (GOOG, AMZN, FB, NFLX, Spotify, etc).
  • Experience building industrial grade ETL pipelines.
  • Experience building frontend systems.
  • Familiarity with dashboards and other visualization tools.
  • Ability to derive generalization bounds for common ML algorithms.
  • Experience developing new machine learning algorithms.

Thank you for illuminating hiring with Quanta Search!
www.quantasearch.com

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