Master's Machine Learning Engineer, Intern

Description

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

The team's stated goal is to increase the GDP of the internet, and a lot of that work is still ahead.

Responsibilities

• The Applied ML, Data Science, Risk, and Payments groups are offering Master's-level machine learning engineering internships for summer 2027.
• The work centers on foundation models that support many tasks, such as fraud detection, improved support, and predicting user behavior.
• You will work on problems that sit where finance, technology, and data meet.
• You may contribute to the company Assistant and the company Foundation Model, which use machine learning to change how businesses work with financial services and data.
• Build and ship large-scale machine learning systems that add significant business value across several areas.
• Own the full cycle of designing, training, refining, and launching models.
• Write production-grade ML models that will be deployed to give a wide range of businesses worldwide access to economic infrastructure.
• Work with other teams, take in their feedback, and look for solutions to problems before they are raised.
• Pick up new tools and methods quickly, ask sharp questions, and keep others clearly informed on your progress.

Requirements

• You need a deep grounding in computer science from a Master's program in Computer Science, Machine Learning, or a closely related field, and must be expected to graduate in winter 2027 or spring/summer 2028.
• You have hands-on experience with programming and machine learning, shown through projects, coursework, or research. You know languages such as Python, Scala, and Spark, and libraries such as Pandas, NumPy, and Scikit-learn.
• You understand machine learning areas such as supervised and unsupervised learning and ML operations. Experience with large language models or reinforcement learning is a plus but not required.
• You have worked on collaborative projects and have taken feedback from different stakeholders and acted on it.
• You can learn unfamiliar systems on your own and make sense of complex ones without much guidance.
• You plan to return to your degree program after the internship or co-op ends.
• Preferred qualifications
• You might also have:
• Two years of university education or equivalent experience, with deep knowledge of particular machine learning domains.
• Peer-reviewed articles published and presented at top-tier venues.
• A record of writing strong pull requests, keeping good test coverage, and finishing projects with few defects.
• Comfort moving through new codebases and working across several programming languages.
• Strong written communication, so you can explain your work clearly to teammates and to wider the company audiences.
• Application materials
• Submit a detailed resume or LinkedIn profile covering your work history.
• Share examples of your relevant work and how you learn, such as GitHub repositories, StackOverflow contributions, or other project portfolios.

Conditions

This is a summer 2027 internship with a Master's-level eligibility requirement and an intent-to-return condition.

Benefits

• The posting frames the role as a chance to have a real effect on the world's economic infrastructure.