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. You Have 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. Nice to Have 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. Technologies We Use and Teach We do not expect candidates to have used our exact stack. We do expect strong production engineering fundamentals, deep domain expertise in intelligent ML systems, and judgment about how ML-derived signals should be used safely in customer-impacting products. Examples of technologies and methods include: Python, Java, Kotlin, SQL. TensorFlow, PyTorch, XGBoost/LightGBM, embeddings, deep learning, and tree-based modeling ecosystems. Kafka or other event-streaming systems, batch data pipelines, feature stores, workflow orchestration, and model-serving systems. Cloud infrastructure, Kubernetes, data warehouses/lakehouses, monitoring, observability, coding agents, evaluation harnesses, and agent-assisted operations tooling. 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 will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.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. While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.  Block takes a market-based approach to pay, and pay may vary depending on your l