[Remote] Staff Applied Machine Learning Engineer - Fraud & Abuse

Note: The job is a remote job and is open to candidates in USA. Block builds simple, powerful tools that make progress towards an economy that’s truly open to all. 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, and other adversarial activities across Block.

Responsibilities

• 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

Skills

• 12+ years building and operating production software and ML systems for business-critical products
• Deep expertise in fraud/risk domains such as payment fraud, identity/account integrity, merchant or marketplace risk, scams, trust & safety, abuse prevention, or compliance decisioning
• Strong production ML judgment across feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response
• Sound judgment around false-positive tradeoffs, noisy labels, adversarial behavior, customer harm, and cross-functional decisions
• Experience using AI-assisted engineering tools with appropriate verification, testing, and review for high-stakes systems
• Experience with graph-based fraud detection, behavioral sequence models, embeddings, entity resolution, anomaly detection, or human-in-the-loop review
• Experience building fraud operations tooling for triage, case management, alert clustering, graph exploration, or policy simulation
• Experience with regulated financial services, model governance, auditability, explainability, or decision logging

Benefits

• Remote work
• Medical insurance
• Flexible time off
• Retirement savings plans
• Modern family planning

Company Overview

Company H1B Sponsorship