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Internship Machine Learning Quant Jobs (NOW HIRING)

IMC Trading

Machine Learning Researcher

Chicago, IL · On-site

IMC Trading is a global trading firm seeking quantitative researchers to apply state-of-the-art machine learning and deep learning to solve challenging trading problems. The role involves designing ...

Visible Hands

Machine Learning Engineer

Manhattan, NY · On-site

$150 - $190/hr

D. in Computer Science, Mathematics, EE, Physics, or a related quantitative field with a focus on Scientific Machine Learning (SciML). * Deep Learning Frameworks: 4+ years of expert-level experience ...

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Internship Machine Learning Quant information

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$25.5K

$42.6K

$88K

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

As of Aug 12, 2026, the average yearly pay for internship machine learning quant 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 the difference between Internship Machine Learning Quant vs Data Scientist Intern?

AspectInternship Machine Learning QuantData Scientist Intern
Required CredentialsStrong programming skills, basic finance knowledge, coursework in machine learningStatistics, programming, domain knowledge, coursework in data analysis
Work EnvironmentFinancial firms, hedge funds, quantitative trading teamsTech companies, startups, research labs
Industry UsageFinance, trading, quantitative researchTechnology, marketing, healthcare analytics
Common Search IntentInternship roles in finance with machine learning focusInternship roles in data science across industries

Internship Machine Learning Quant roles typically focus on applying machine learning techniques to financial data within trading and investment firms. Data Scientist Intern positions are broader, spanning various industries like tech and healthcare, emphasizing data analysis and modeling. While both require programming and analytical skills, the finance-specific knowledge is more critical for Machine Learning Quant internships.

More about Internship Machine Learning Quant jobs
What cities are hiring for Internship Machine Learning Quant jobs? Cities with the most Internship Machine Learning Quant job openings:
What are the most commonly searched types of Machine Learning Quant jobs? The most popular types of Machine Learning Quant jobs are:
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What job categories do people searching Internship Machine Learning Quant jobs look for? The top searched job categories for Internship Machine Learning Quant jobs are:
Internship Machine Learning Quant jobs near you
Infographic showing various Internship Machine Learning Quant job openings in the United States 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 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Quantitative Researcher - Machine Learning

Point72

New York, NY • On-site

Full-time

Re-posted 6 days ago

Job description

About Cubist
Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.
Role/Responsibilities:
We are seeking a quantitative researcher for the Cubist Machine Learning Research group with experience in machine learning, especially recent deep learning and natural language processing technology.
Researchers will use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave. Successful researchers manage all aspects of the research process including data ingestion and processing, data analysis, methodology selection, implementation and testing, prototyping, and performance evaluation.
Researchers will be introduced to industry standard datasets, including understanding which data may be relevant to a certain model or financial problem; how to collect, parse, and clean the data; how to incorporate the data into innovative functional models; how to construct and develop features from raw data; and how to estimate effectiveness of such features.
Researchers will also be provided with the opportunity to implement the full breadth of their knowledge and training to actively participate in all stages of research & development of financial models through use of machine learning. Based on experience from working with existing industry-standard models and algorithms, researchers will learn how to construct their own models in order to solve complex financial problems and enhance data prediction capabilities within the financial services industry.
Requirements:
  • PhD or PhD candidate in machine learning, computer science, statistics, or a related field
  • Experience with sequential modeling and time series forecasting using deep learning
  • Experience with deep neural networks and representation learning
  • Prior experience working in a data driven research environment
  • Experience with translating mathematical models and algorithms into code
  • Proficient in programming languages such as Python and R
  • Experience with machine learning software libraries such as TensorFlow or PyTorch
  • Experience with natural language processing technology a strong plus
  • Excellent analytical skills, with strong attention to detail
  • Interest in applying machine learning to finance
  • Collaborative mindset with strong independent research ability
  • Strong written and verbal communication skills

We're looking for exceptional colleagues with unparalleled passion. If you'd like your resume to stand out, tell us about your exceptional personal achievements, even if they have nothing to do with finance. Of course we love to hear more about specific engineering or data projects that you've worked outside of school, or as part of your curriculum. If you're proud of the work you did we want to hear about it. In addition to exceptional statisticians and engineers, we work with talented musicians, writers, mathematicians, and founders of non-profits; we'd love to learn more about what excites you.

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