Staff Machine Learning Engineer, Predictive Systems

Block is a technology company focused on increasing access to the global economy. They are seeking a Staff Machine Learning Engineer for their Predictive Systems team to lead technical strategies for machine learning and decisioning systems that manage risk, fraud, and abuse across their platforms.

Responsibilities:

• Act as a technical lead across multiple Risk ML engineering teams, setting direction for how ML and AI solutions are applied to fraud, abuse, and risk decisioning
• Design and evolve real-time and batch ML systems that leverage Block's core ML platforms to deliver scalable risk solutions
• Drive architectural decisions around feature usage, contextual signals, and ML-driven controls in partnership with modeling, product, and platform teams
• Translate ambiguous risk problems into clear technical strategies and execution plans
• Lead cross-team efforts to integrate new data sources, signals, and event hooks into production risk systems
• Establish standards for data quality, observability, and correctness for ML inputs and decisions
• Provide technical leadership during emerging threat and SEV scenarios, guiding rapid mitigation while preserving long-term system health
• Influence ML and AI engineering practices across Risk, improving consistency, reliability, and velocity

Requirements:

• Extensive experience designing and leading large-scale ML systems with broad organizational impact
• A proven ability to set technical vision and drive alignment across multiple teams and disciplines
• Deep understanding of ML systems, data platforms, and real-time decisioning at scale
• Strong judgment in navigating complex tradeoffs involving risk, trust, and product velocity
• A history of mentoring senior engineers and developing future technical leaders
• A systems-level mindset and a bias toward durable, high-leverage solutions
• Act as a technical lead across multiple Risk ML engineering teams, setting direction for how ML and AI solutions are applied to fraud, abuse, and risk decisioning
• Design and evolve real-time and batch ML systems that leverage Block's core ML platforms to deliver scalable risk solutions
• Drive architectural decisions around feature usage, contextual signals, and ML-driven controls in partnership with modeling, product, and platform teams
• Translate ambiguous risk problems into clear technical strategies and execution plans
• Lead cross-team efforts to integrate new data sources, signals, and event hooks into production risk systems
• Establish standards for data quality, observability, and correctness for ML inputs and decisions
• Provide technical leadership during emerging threat and SEV scenarios, guiding rapid mitigation while preserving long-term system health
• Influence ML and AI engineering practices across Risk, improving consistency, reliability, and velocity