PhD Machine Learning Engineer, Intern

Description

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

The internship sits within the Applied ML, Data Science, Risk and Payments groups.

Responsibilities

• Build and ship large-scale machine learning systems that create measurable value across several areas of the business.
• Take models through the full cycle of design, training, refinement and launch.
• Write production-grade ML models that go live and extend access to economic infrastructure for businesses of many kinds worldwide.
• Work on foundation models that serve dozens of tasks, such as fraud detection, improved support and predicting user behavior.
• Contribute to a company assistant and a foundation model that use machine learning to change how businesses engage with financial services and data.
• Work with other teams, act on feedback and look for solutions to problems on your own initiative.
• Pick up new tools and methods quickly, ask sharp questions and report clearly on where your work stands.
• Tackle hard problems where finance, technology and data meet.

Requirements

• Be working toward a PhD in computer science, machine learning or a closely related field, expecting to graduate in winter 2027 or spring/summer 2028.
• Have practical programming and ML experience from projects, coursework or research, with working knowledge of Python, Scala, Spark, and libraries such as Pandas, NumPy and Scikit-learn.
• Have expertise in supervised and unsupervised learning and ML operations, and possibly in large language models or reinforcement learning.
• Have worked on team projects and taken in and acted on feedback from different stakeholders.
• Learn unfamiliar systems on your own and understand complex systems without close guidance.
• Plan to return to your degree program after the internship.
• Preferred qualifications: 2 years of university education or equivalent experience, with in-depth knowledge of specific machine learning domains.
• You might also have: published and presented peer-reviewed articles in top-tier venues.
• You might also have: a record of writing high-quality pull requests, keeping good test coverage and finishing projects with few defects.
• You might also have: experience moving through new codebases and working across several programming languages.
• You might also have: strong written communication for both team members and wider company audiences.
• Submit a detailed resume or LinkedIn profile covering your work history.
• Share examples of relevant work and how you learn, such as GitHub repositories, StackOverflow contributions or other project portfolios.

Conditions

The internship runs for the summer of 2027.