Senior Machine Learning Engineer, Applied AI Quality

Block (Square)·Bay Area, CA, United States of America·onsite
crypto:applicationengineeringIC510409 Engineering - Applied AI
Compensation
Not disclosed
Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block. The Role At Block, we believe product quality is foundational to great user experiences, and AI is transforming how we measure, understand, and improve that quality at scale. Our team builds the intelligence layer that evaluates system behavior across millions of real-world interactions, helping ensure our products are reliable, safe, and continuously improving. We’re looking for a Senior Machine Learning Engineer to lead the technical direction of next-generation quality systems powered by LLMs and AI agents. You’ll drive the architecture and strategy behind systems that evaluate product behavior, surface emerging issues, generate actionable insights, and enable teams across Block to make higher-confidence product decisions. In this role, you’ll operate across ambiguous, high-impact problem spaces and shape how quality is measured and operationalized across the organization. You’ll work across engineering, product, platform, and leadership teams to define long-term technical direction, establish scalable evaluation frameworks, and build systems that become foundational infrastructure for AI-driven product quality. You Will Lead the technical strategy and architecture for AI-driven quality and evaluation systems used across products and teams. Drive the development of scalable systems that use LLMs, agents, and behavioral signal