It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world’s relationship with money to make it more relatable, instantly available, and universally accessible.

Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We’ve been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.

The Role

This opportunity is only open to students who are currently enrolled in either a Masters or PhD program and offers an 8-month term (with a possibility for extension) on Cash App's Risk AI team starting in Winter of 2024/2025. If you've already graduated (or are set to graduate before May 2025) please apply to one of our full-time roles.

This role is for Block's AI R&D team, which pursues an ambitious agenda of fundamental and applied research. We are a highly creative group that prefers to solve problems from first principles, while moving quickly. At the moment, we are actively working in the areas of Graph Representation Learning, Sequential Modeling, Explainability, and Sequential Decision Making; and we're looking for candidates with experience in these topic areas. We have shown that these technologies have the power to transform the way that Block detects fraudulent behaviour, and are continuing to invest in them.

You Will

You will be working closely with various team members while driving your own research project. Over the course of your internship you might:

• Develop new, standardized, datasets, either for internal use or for external release
• Provide quantitative assessment of the advantages and disadvantages of different model classes based on accuracy, precision/recall, training cost, training time, data requirements, and interpretability
• Explore the mathematical foundations of various model classes to understand their representational ability
• Develop new algorithms or state of the art models for tasks of interest
• Communicate your project status and results frequently

Successful outcomes for projects could include production deployments of your models, or publication of papers based on your work.

You Have

• 1-2 years of experience with applied Deep Learning
• Proven ability to implement in practice neural network architectures described in literature using deep learning frameworks such as PyTorch
• Demonstrated ability to lead a research project from ideation to publication
• Clear communication skills
• A curious, passionate, growth-oriented mindset

Technologies We Use and Teach

• Python, Pytorch, Numpy, Pandas, SQL

We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We also consider qualified applicants with criminal histories for employment on our team, and always assess candidates on an individualized basis.

We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we’re doing to build a workplace that is fair and square? Check out our I+D page.

Block will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.