Master's Machine Learning Engineer, Intern

Description

The company runs a financial infrastructure platform for businesses. Millions of companies, from large enterprises to early-stage startups, use it to take payments, grow revenue, and open new business opportunities.

The Applied ML, Data Science, Risk, and Payments groups are hiring machine learning engineering interns for summer 2027. The work covers projects that improve the company's products, including foundation models that power dozens of tasks such as fraud detection, better support, and user behavior prediction.

Responsibilities

• Work on hard problems where finance, technology, and data meet, including the company's assistant and foundation model projects, which apply machine learning to how businesses use financial services and data.
• Build and ship large-scale machine learning systems that create significant business value in several areas.
• Handle the full cycle of designing, training, improving, and launching machine learning models.
• Write production-grade models that help the company give businesses around the world access to financial infrastructure.
• Work with other teams, act on their feedback, and look for solutions to problems before they are asked for.
• Pick up new tools and methods quickly, ask sharp questions, and report clearly on where your work stands.

Requirements

• Be pursuing a Master's degree in Computer Science, Machine Learning, or a closely related field, and expect to graduate in winter 2027 or spring/summer 2028.
• Have hands-on programming and machine learning experience from projects, coursework, or research. Familiarity with Python, Scala, or Spark, and with Pandas, NumPy, or Scikit-learn, is expected.
• Know supervised and unsupervised learning methods and ML operations. Experience with large language models or reinforcement learning is possibly needed.
• Have worked on team projects and taken in and used feedback from different stakeholders.
• Learn unfamiliar systems on your own and make sense of complex systems independently.
• Plan to return to your degree program after the internship or co-op.
• Send a detailed resume or LinkedIn profile covering your work history, plus examples of your work and learning, such as GitHub repositories, StackOverflow contributions, or other portfolios.
• Preferred qualifications: 2 years of university education or equivalent experience, with deep knowledge of a specific area of machine learning.
• Preferred qualifications: peer-reviewed papers published and presented at top-tier venues.
• Preferred qualifications: a record of high-quality pull requests, solid test coverage, and projects delivered with few defects.
• Preferred qualifications: comfort moving through new codebases and switching between programming languages.
• Preferred qualifications: strong written communication, so you can explain your work to both your team and wider company audiences.

Conditions

The internship is for summer 2027.