PhD Machine Learning Engineer, Intern

Description

The company provides financial infrastructure for businesses. Millions of firms, from very large enterprises to early-stage startups, use it to take payments, expand their revenue, and open new commercial opportunities.

Responsibilities

• Join the summer 2027 internship run by the applied ML, data science, risk, and payments groups, doing PhD-level machine learning engineering.
• Help improve company products through work on foundation models that support dozens of tasks, including fraud detection, better support, and user behavior prediction.
• Contribute to an internal AI assistant and a foundation model that apply machine learning to how businesses interact with financial services and data.
• Tackle hard problems where finance, technology, and data meet.
• Build and deploy 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 businesses worldwide access to economic infrastructure.
• Work with other teams, act on their feedback, and take initiative in solving problems.
• Pick up new technologies and methods quickly, ask sharp questions, and report clearly on where your work stands.

Requirements

• Be pursuing a PhD in computer science, machine learning, or a closely related field, and expect to graduate in winter 2027 or spring/summer 2028.
• Show hands-on programming and ML skill through projects, coursework, or research, including languages such as Python, Scala, and Spark, and libraries such as Pandas, NumPy, and Scikit-learn.
• Have expertise in areas such as supervised and unsupervised learning and ML operations, and possibly in large language models or reinforcement learning.
• Have worked on collaborative projects and acted on feedback from a range of stakeholders.
• Learn unfamiliar systems on your own and understand complex systems without close guidance.
• Intend to return to your degree program after the internship.
• Provide a detailed resume or LinkedIn profile covering your work history.
• Share examples of your work and how you learn, such as GitHub repositories, StackOverflow contributions, or other project portfolios.
• Preferred qualifications: 2 years of university education or equivalent experience, with deep knowledge of specific machine learning domains.
• You Might Also Have: peer-reviewed articles published and presented in top-tier venues.
• You Might Also Have: a record of writing high-quality pull requests, keeping strong test coverage, and finishing projects with few defects.
• You Might Also Have: comfort navigating new codebases and working across several programming languages.
• You Might Also Have: strong written communication for explaining your work to teammates and wider audiences.

Conditions

The internship takes place in summer 2027.