Senior Machine Learning Engineer, Risk Modeling

Block•Seattle, WA

7d•$160,700 - $283,600

About The Position

Block builds technology to increase access to the global economy. Across our ecosystem, including Square and Cash App, we create tools that help businesses run and grow, and individuals move, manage, and grow their money with confidence. Square empowers sellers of all sizes with integrated, omnichannel tools to accept payments, manage operations, access financial services, and reach customers across online and in-person channels. Cash App complements this by providing a fast, accessible financial platform for millions of people to send, spend, save, invest, and borrow, helping redefine how individuals interact with money.Operating at massive scale across both ecosystems means trust and safety are foundational. Our teams build systems that protect real people and businesses, safeguard financial activity, and ensure our products remain reliable, secure, and easy to use. Block is a global, distributed company with a culture rooted in ownership, creativity, and impact. Whether supporting sellers on Square or customers on Cash App, we’re united by a shared mission: to make the global economy more accessible and inclusive. We’re hiring Senior Machine Learning Engineers to join Block’s Risk Machine Learning organization, where teams apply ML at massive scale to detect, prevent, and reduce fraud and abuse across Cash App and Square. This opening supports multiple senior-level roles, with team placement determined through a collaborative matching process based on your experience, interests, and current business needs. Today, we’re growing teams focused on chargeback and fraud prevention as well as trust and safety initiatives supporting Cash App Families and teen banking. Across teams, your work will directly protect our ecosystem, reduce financial loss, and enable safe, seamless financial experiences for millions of customers, sellers, and families. Team Focus Areas Depending on your background and interests, you may join a team focused on one of the following areas: Fraud & Chargeback Risk Build machine learning models and systems that detect fraudulent transactions, reduce chargebacks, and protect the integrity of payments across Cash App and Square. Trust, Safety & Families Develop machine learning systems that enable safe and trustworthy financial experiences for teens and families by detecting risky or abusive behavior, strengthening account integrity, and supporting long-term trust in Cash App. Both teams value first-principles thinking, rapid iteration, and shipping production ML solutions with real-world impact.

Requirements

2+ years of industry experience in machine learning, applied AI, or related fields
Bachelor’s degree in a quantitative field (Computer Science, Engineering, Statistics, Physics, Applied Math); Master’s or PhD preferred
Proven experience independently designing, deploying, and maintaining ML solutions in production
Strong familiarity with techniques such as tree-based models, deep learning, transfer learning, or reinforcement learning
Experience influencing technical direction and collaborating with cross-functional partners at scale
Strong communication skills, sound judgment, and an ownership mindset
Curiosity and alignment with Block’s mission of economic empowerment

Responsibilities

Partner with product, engineering, data science, policy, and operations to design and productionize ML-driven risk solutions at scale
Own end-to-end machine learning systems, from problem definition and modeling to deployment, monitoring, and iteration
Lead technical decision-making within your workstreams and influence ML strategy and planning
Build tooling and processes that improve the speed, reliability, and impact of the ML development lifecycle
Apply state-of-the-art modeling techniques and third-party data sources to improve detection and decision-making
Investigate emerging fraud, abuse, and risk patterns to proactively inform product safeguards and policy
Collaborate closely with ML platform and engineering teams to ensure models operate reliably in real time and at scale