Staff Applied Machine Learning Engineer - Fraud & Abuse

As a Staff Applied Machine Learning Engineer focused on Fraud & Abuse at Block, you will design, build, and operate production machine learning (ML) decision systems aimed at reducing payment fraud, account takeovers, identity abuse, merchant and marketplace risks, scams, and other adversarial activities across Block's platforms. Your role will involve integrating diverse signals into low-latency serving and batch scoring systems, managing feature pipelines, and overseeing the entire model lifecycle. You will collaborate closely with ML modelers, product engineers, risk analysts, compliance partners, and operations teams to ensure the development of reliable and safe ML systems that balance fraud reduction with legitimate customer access.

In this position, your key responsibilities will include building and operating real-time and batch ML decisioning systems for various fraud and abuse prevention scenarios. You will integrate behavioral, graph, device, network, event-stream, and third-party signals into model serving, decision APIs, and product controls. Managing the production lifecycle for risk decisions will involve tasks such as data contracts, feature quality assurance, monitoring, drift detection, safe rollout, rollback, and incident response. Additionally, you will develop feedback loops and AI-assisted workflows for triage, investigation support, alert clustering, graph exploration, simulation, and post-incident learning.

To be considered for this role, you should have over 12 years of experience in building and operating production software and ML systems for business-critical products. A deep expertise in fraud and risk domains, including payment fraud, identity and account integrity, merchant or marketplace risk, scams, trust and safety, abuse prevention, or compliance decisioning, is essential. Strong production ML judgment across feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response is required. You should also possess sound judgment regarding false-positive tradeoffs, noisy labels, adversarial behavior, customer harm, and cross-functional decisions. Experience with AI-assisted engineering tools, including appropriate verification, testing, and review for high-stakes systems, is also necessary.

While specific compensation details are not provided, Block offers a market-based approach to pay, with salaries varying depending on location and other factors. The company provides comprehensive benefits, including healthcare coverage (medical, vision, and dental insurance), health savings and flexible spending accounts, retirement plans with company match, employee stock purchase programs, wellness programs, paid parental and caregiving leave, paid time off (including 12 paid holidays), paid sick leave, learning and development resources, and paid life insurance, AD&D, and disability benefits.

Block is committed to building a more inclusive economy and strives to create a workplace that reflects these values. The company encourages applicants to share any needed accommodations with their recruiter, who will treat these requests confidentially. Block is an and evaluates all employees and job applicants consistently, based solely on the core competencies required for the role.