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 leveraging Generative AI (specifically Large Language Models) and Machine Learning as an integral 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 also proactively block payments in real time where appropriate. Now, we are also leveraging generative AI technologies to improve agent workflow and case review tools; by adding features that accelerate agent productivity and enable them to make faster, more informed and accurate decisions.
This is a new and significant opportunity to rethink and optimize Financial Crimes Operations at CashApp scale. This is an IC role, but the Staff level has significant leadership responsibilities that include owning, and driving strategic roadmaps & priorities to completion by collaborating with relevant cross functional stakeholders.
You will:

• Design, build, integrate and enhance batch inference services and tooling that support our LLM Copilot and Autopilot use cases
• Unblock the modelers on the team with the infrastructure/tools necessary for development & production work including solving MLOps, data access, security and scalability issues
• Develop prototypes and partner with modelers to encourage adoption of new tools and technologies and proactively identify and plan for future needs of our modelers
• Lead by example by applying AI/ML and engineering best practices
• Partner closely with Financial Crimes, AI/ML Platform and Product Engineering teams across Cash App and Block

Qualifications

• 5+ years of combined Machine Learning and Engineering industry experience (full stack ML experience is strongly preferred)
• A Masters or advanced degree in computer science, data science, operations research, applied math, stats, physics, or a related technical field
• Familiarity with Linux/OS X command line, version control software (git), and general software development principles with a machine learning software development life-cycle orientation.
• Experience working with product, business, and engineering to prioritize, scope, design, and deploy ML models
• Familiarity with Python computing stack, MySQL, Snowflake, Airflow, Java/Go
• Hosted models for inference on public clouds like GCP, AWS and/or built micro-services to facilitate event based triggering, feature generation, model inference and downstream actioning.

Qualifications

• 5+ years of combined Machine Learning and Engineering industry experience (full stack ML experience is strongly preferred)
• A Masters or advanced degree in computer science, data science, operations research, applied math, stats, physics, or a related technical field
• Familiarity with Linux/OS X command line, version control software (git), and general software development principles with a machine learning software development life-cycle orientation.
• Experience working with product, business, and engineering to prioritize, scope, design, and deploy ML models
• Familiarity with Python computing stack, MySQL, Snowflake, Airflow, Java/Go
• Hosted models for inference on public clouds like GCP, AWS and/or built micro-services to facilitate event based triggering, feature generation, model inference and downstream actioning.