Company Description
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 47 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.
Check out our locations, benefits, and more at cash.app/careers.
Job Description
The Financial Crimes Technology team at Cash App detects and reports illegal and suspicious activity on Cash App. We work globally with partners in Product, Counsel and Engineering to ensure that we are providing a safe user experience for our customers while minimizing or eliminating bad activity on our platform.
We are using Machine Learning and Generative AI as an important part of our toolkit to fulfill our mission. As Cash App scales, we monitor hundreds of billions of dollars in transactions across traditional payment and blockchain networks. Our machine learning systems monitor and surface suspicious activity (money laundering, illegal activity and terms of service violations) for agent review. Our systems block payments in real-time where appropriate. Additionally, we use generative AI technologies to improve agent workflow and case review tools, by adding features that accelerate agent productivity and allow them to make more informed and accurate decisions.
This is an IC role, but the senior level has leadership responsibilities including leading strategic roadmaps and priorities to completion by collaborating with relevant cross functional stakeholders.
You will:

• Facilitate CashApp's ML based Customer Risk Rating program to detect onboarding and ongoing risk and satisfying Know Your Customer (KYC) and Know Your Business (KYB) requirements
• Build classification models to detect illegal use of the app across the peer-to-peer, banking, card, equities and bitcoin products
• Leverage diverse data sets including payment transactions, connected users and asset graphs, unstructured text data and user profile information to build ML and generative AI models.
• Experiment and deploy AI copilot and self-driving solutions at scale to improve agent productivity and/or eliminate manual decision loops altogether
• Work with the embedded Machine Learning Engineers on the team and ML platform services to deploy models to the production environment and monitor ongoing performance
• Use Python ML stack, LLMs, Pytorch, Snowflake, Airflow based tools, and cloud services (both GCP & AWS)

Qualifications
You Have:
4+ years of Machine Learning modeling experience. Full stack ML experience

• A Bachelor's degree in computer science, data science, operations research, applied math, stats, physics, or a related technical field
• End-to-end experience building and deploying ML to production systems (batch and real-time) that are performant at scale
• Experience with advanced ML techniques like large language models, embeddings, sequence modeling, and graph convolutional networks
• Experience of independently driving programs with multiple cross functional stakeholders (eg. Engineering, Product, and Country leads) that have business impact
• Have a curious, growth-oriented mindset and the ability to think in first principles to identify creative solutions that demonstrate value

Qualifications
You Have:
4+ years of Machine Learning modeling experience. Full stack ML experience

• A Bachelor's degree in computer science, data science, operations research, applied math, stats, physics, or a related technical field
• End-to-end experience building and deploying ML to production systems (batch and real-time) that are performant at scale
• Experience with advanced ML techniques like large language models, embeddings, sequence modeling, and graph convolutional networks
• Experience of independently driving programs with multiple cross functional stakeholders (eg. Engineering, Product, and Country leads) that have business impact
• Have a curious, growth-oriented mindset and the ability to think in first principles to identify creative solutions that demonstrate value