Staff Applied Machine Learning Engineer - Fraud & Abuse
$276,800–$415,200 year
Remote
Job Summary
Staff Applied Machine Learning Engineer focused on Fraud & Abuse at Block. 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. Own end-to-end production lifecycle including data contracts, feature quality, online/offline consistency, monitoring, drift detection, safe rollout, rollback, and incident response. Build real-time and batch ML decisioning, integrate behavioral and graph signals into low-latency serving, and collaborate with modelers, product, risk, compliance, and operations to balance fraud losses with user access. Strong emphasis on scalable, reliable systems with measurable customer outcomes. Requires 12+ years in production software/ML, deep fraud/risk domain expertise, and experience with production ML tooling, monitoring, and governance. Technologies include Python, Java, Kotlin, SQL; TensorFlow, PyTorch, XGBoost/LightGBM; Kafka; cloud/Kubernetes; and related data/ops tooling. Block is an equal opportunity employer and supports remote work as part of its offering.
Required Qualifications
- 12+ years building and operating production software and ML systems
- 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
- Experience using AI-assisted engineering tools with appropriate verification, testing, and review for high-stakes systems
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