Senior Machine Learning Engineer, Applied AI Quality

Block•Bay Area, CA, United States of America, CA

6d•$194,500 - $343,100•Remote

About The Position

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.

Requirements

5+ years of experience in software engineering, machine learning engineering or applied AI.
Deep experience designing and shipping large-scale AI/ML systems in production environments.
Strong expertise with LLMs, agents, evaluation systems, retrieval architectures, and modern AI infrastructure.
Proven ability to lead ambiguous, high-impact technical initiatives from concept through adoption across multiple teams.
Strong systems thinking and architectural judgment, with the ability to balance experimentation, scalability, and operational rigor.
Experience defining technical strategy and influencing roadmaps beyond your immediate team.
Excellent communication and cross-functional leadership skills, with the ability to align engineering, product, and organizational priorities.
A track record of creating leverage through platforms, frameworks, and systems that enable other engineers and teams to move faster and make better decisions.

Responsibilities

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 signals to evaluate quality, detect regressions, and generate product insights.
Define long-term approaches for evaluation, measurement, and quality intelligence across complex product surfaces.
Translate ambiguous organizational needs into clear technical direction, roadmap priorities, and platform capabilities.
Influence engineering standards and best practices for building reliable, measurable, and trustworthy AI systems.
Lead complex cross-functional initiatives spanning product, infrastructure, data, and applied AI teams.
Mentor and level up engineers across the organization through technical leadership, design reviews, and systems thinking.
Identify leverage opportunities where AI systems can fundamentally improve how teams understand, debug, and improve product behavior.