Staff Machine Learning Engineer

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

Machine Learning is an integral part of how we design products, operate, and pursue Cash App’s mission to serve the unbanked as well as disrupt traditional financial institutions. Our massive scale and deep trove of transaction data create an endless number of opportunities to use artificial intelligence to better understand our customers and offer new products and experiences that can improve their lives. We are a highly creative group that prefers to solve problems from first principles; we move quickly, make incremental changes, and deploy to production every day. Check out our Machine Learning / AI blog here!

This role is part of our Cash App’s Underwriting and Credit ML team. This team’s mission is to redefine credit and liquidity through simple, fair, and accessible credit solutions that build financial health and resilience.

You Will

• Build and integrate ML solutions for evaluating customer cash flow risk including risk of default on credit obligations, risk of fraud & abuse of loans and other situations where Cash app advances funds or provides liquidity to customers
• Support multiple products across 4+ workstreams in Cash
• Empower teams to understand the financial performance of origination cohorts, optimize our automated decisioning pipeline using AI/ML, and help identify new opportunities for growth in our customer base
• Experiment with various modeling techniques on our comprehensive customer data and see your solution through to production by partnering cross-functionally with finance, product, and engineering teams

You Have

• Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field. A Master’s degree is preferred
• 6+ years of experience in machine learning, data analysis, or a related field
• Strong knowledge of machine learning algorithms and data analysis techniques
• Excellent problem-solving skills and attention to detail
• Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders

Technologies We Use and Teach

• Python (NumPy, Pandas, sklearn, xgboost, TensorFlow, keras, etc.)
• MySQL, Snowflake, GCP/AWS and Tableau

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.