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Internship Machine Learning Finance Jobs (NOW HIRING)

Jane Street

Machine Learning Engineer

New York, NY · On-site

Machine learning is a critical pillar of Jane Street's global business. Our ever-changing trading ... internship program here. About You If you've never thought about a career in finance, you're in ...

Jane Street

Machine Learning Engineer

New York, NY

Machine learning is a critical pillar of Jane Street's global business. Our ever-changing trading ... internship program here . About You If you've never thought about a career in finance, you're in ...

Jane Street

Machine Learning Researcher

New York, NY · On-site

You'll spend the bulk of your internship working closely with full-time machine learning ... About You If you've never thought about a career in finance, you're in good company. Many of us ...

Jane Street

Machine Learning Researcher

New York, NY · On-site

You'll spend the bulk of your internship working closely with full-time machine learning ... About You If you've never thought about a career in finance, you're in good company. Many of us ...

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 ...

Yaskawa

Machine Learning Engineer

Franklin, WI · On-site

To be successful you should have 0-3 years of of professional or internship experience in machine learning, data science, or software engineering. Also proficiency in Python and familiarity with ML ...

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Internship Machine Learning Finance information

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

As of Jul 9, 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.

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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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Internship Machine Learning Finance jobs near you
Infographic showing various Internship Machine Learning Finance job openings in the United States as of July 2026, with employment types broken down into 92% Full Time, 5% Part Time, and 3% Contract. Highlights an 83% Physical, 5% Hybrid, and 12% Remote job distribution, with an average salary of $41,299 per year, or $19.9 per hour.
Internship - Machine Learning Engineer

Internship - Machine Learning Engineer

Smule

Salt Lake City, UT • Remote

Other

Re-posted 6 days ago

Job description

Salary:

Smule has been on a mission to bring the world together through music since 2008. Music is much more than listening it's about creating, sharing, discovering, participating, and connecting with people. With dozens of millions of monthly active users creating over 20 million songs every day, Smule is connecting people all over the world through the joy of making music and transforming the music landscape from one of passive listening to collaborative creative expression and active engagement.


About the Role:

We are looking for a Machine Learning Engineer to own the end-to-end lifecycle of ML models in production at Smule, from training and optimization through deployment, monitoring, and iteration. You will work closely with research scientists to bring models off the bench and into scalable, reliable systems that serve millions of users. The ideal candidate is a strong engineer first, with deep practical knowledge of ML systems, a passion for reliability, and an eye for performance.


We strongly encourage candidates with non-traditional backgrounds to apply. If your path into ML engineering came through backend systems, DevOps, audio software, data engineering, or another field, we want to hear from you.


What You'll Be Doing:

  • Design, build, and maintain production ML pipelines encompassing data ingestion, feature engineering, model training, evaluation, and deployment.
  • Optimize models for production constraints including latency, throughput, memory footprint, and cost, using techniques such as quantization, distillation, pruning, and efficient serving architectures.
  • Implement robust monitoring, alerting, and observability for deployed models, covering data drift, prediction quality, and system health.
  • Collaborate with research scientists to integrate new model architectures and training techniques into production systems with minimal friction.
  • Build and improve CI/CD pipelines for ML, including automated testing, validation gates, and staged rollouts.
  • Manage compute infrastructure and costs, making informed tradeoffs between performance, reliability, and budget.


What We're Looking For:

  • Degree (B.S., M.S., or Ph.D.) in Computer Science, Software Engineering, Electrical Engineering, or a related technical discipline, or currently pursuing one.
  • Strong proficiency in Python and experience with deep learning serving (TorchServe, Triton, vLLM, or equivalent).
  • Solid understanding of systems engineering: networking, storage, containerization, orchestration, and monitoring.
  • Ability to reason about tradeoffs between latency, throughput, cost, and model quality.


Bonus Points For:

  • Experience serving large language models or other generative models at scale.
  • Familiarity with audio/music processing pipelines and real-time inference constraints.
  • Experience with Bayesian optimization, bandit algorithms, or adaptive experimentation platforms.
  • Contributions to open-source ML infrastructure projects.


Smule is an Equal Opportunity Employer and considers all qualified applicants without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, ancestry, age, disability, medical condition, genetic information, marital status, military or veteran status, or any other protected characteristic under federal, state, or local law.


We are committed to creating an inclusive environment for all employees and applicants. If you require a reasonable accommodation during the application or interview process, please let us know.