Senior Machine Learning Engineer, Risk ML Evaluations

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

Cash App holds people’s money, and maintaining customers’ trust is absolutely essential to our brand. Preventing scams and fraud is a major component of building that trust. 

The Risk ML Evaluations team's mission is to prevent fraud and keep our customers safe on our platform. We do that by developing and operating large scale risk pipelines and models to evaluate thousands of transactions per second in real time. We closely collaborate with ML Modelers and Software Engineers from across Cash App to incorporate new features and new models to advance our fraud detection capabilities. Across the team, we employ methods ranging from simple heuristics to Deep Learning technology to improve our performance and services. We’re also excited to explore new directions with the team to continue pushing the boundaries of what’s possible.

You Will

• Design, build, and maintain Machine Learning systems that enable us to flag fraudulent activities in real-time
• Work hand-in-hand with ML Modelers to identify and integrate new data sources, heuristics and models
• Solve challenging technical problems at scale, collaborating with colleagues located across the globe
• Own your solutions from design through to operation: we all share the pager!

You Have

• 8+ years of experience in software development and demonstrated technical initiative and leadership on previous machine learning projects
• Work autonomously in a fast paced, ambiguous and unpredictable environment
• Natural curiosity & eagerness to learn
• Ability to work creatively, taking initiative and leading when required
• Interest in owning your solutions, maintaining and fixing them as necessary
• Communicate via clear and concise writing to facilitate asynchronous collaboration across multiple time zones
• Reason about complex, distributed systems at high scale
• Proficiency in machine learning techniques, experimental design and data engineering
• Strong programming skills in languages such as Kotlin, Python, TensorFlow, or PyTorch

Technologies We Use and Teach

• Python, Java, Kotlin
• Kubernetes, AWS
• MySQL, DynamoDB
• Datadog
• HTTP, JSON, gRPC, Protocol Buffers
• Kafka, event-driven microservice architecture

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.

Block takes a market-based approach to pay, and pay may vary depending on your location. U.S locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.

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