Staff Applied Machine Learning Engineer - Fraud & Abuse

Block builds simple, powerful tools that make progress towards an economy that’s truly open to all.

Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we’re helping build a financial system that is open to everyone. Join us.

The Role

As a Staff Applied Machine Learning Engineer focused on Fraud & Abuse, you will design, build, and operate production ML decision systems that reduce payment fraud, account takeover, identity abuse, merchant and marketplace risk, scams, and other adversarial activity across Block.

The team optimizes for reliable decisions, safe deployment, and measurable customer outcomes — preserving access for good customers while reducing fraudulent, abusive, or unsafe activity.

You should be comfortable owning production systems end to end: data contracts, low-latency inference, batch scoring, feature quality, online/offline consistency, model deployment, monitoring, incident response, rollback, and outcome feedback loops. The work combines large-scale ML decisioning with AI-assisted operations: surfacing evidence, simulating controls, accelerating triage, and improving feedback loops while preserving human judgment in high-stakes decisions.

You will work closely with ML modelers, product engineers, risk analysts, compliance partners, and operations teams to respond quickly to evolving abuse patterns without creating unnecessary friction or harm for legitimate customers.

You Will

• Build and operate real-time and batch ML decisioning systems for payment fraud, scams, identity and account integrity, merchant and marketplace risk, and abuse prevention.
• Integrate behavioral, graph, device, network, event-stream, and third-party signals into low-latency model serving, decision APIs, and product controls.
• Own the production lifecycle for risk decisions, including data contracts, feature quality, online/offline consistency, monitoring, drift detection, safe rollout, rollback, and incident response.
• Develop feedback loops and verified AI-assisted workflows for triage, investigation support, alert clustering, graph exploration, simulation, and post-incident learning.
• Partner with modelers, analysts, product, compliance, and operations to balance fraud losses, customer access, false positives, product velocity, support burden, and long-term trust.
• Create reusable decision and evaluation capabilities that product services, internal tools, and AI-assisted workflows can safely consume.