Staff Machine Learning Engineer

Description:

Block is a global technology company committed to building an inclusive financial system. The Advanced Insights and Modeling (AIM) team, consisting of machine learning engineers, data scientists, and strategists, focuses on developing AI-driven predictive metrics that guide strategic investments and operational excellence across the organization. These predictive metrics include revenue inflow/outflow, customer lifetime value, sales prospect value, and more—informing decisions across Finance, Marketing, Sales, Product, and Risk.

The Staff Machine Learning Engineer (Modeler) will lead innovation in metrics prediction, designing and implementing end-to-end ML solutions to enhance Block’s value prediction systems for both prospective and existing customers. This role involves close collaboration with cross-functional teams to refine growth strategies, engage with third-party data sources, and integrate models into scalable ML systems supporting long-term company growth.

You will leverage a wide range of machine learning techniques, from interpretable models like XGBoost to advanced neural networks, to forecast customer value, segment opportunities, and optimize business outcomes. Beyond modeling, you will also contribute to the development of Block’s AI infrastructure, empowering teams to explore data, generate insights, and make data-driven decisions effectively.

Key Responsibilities:

• Lead innovation in predictive metrics to support strategic planning and growth initiatives.

• Design, develop, and maintain production-grade ML systems with robust data pipelines.

• Build customer lifetime value and related prediction models using classical and deep learning techniques.

• Collaborate with Finance, Sales, Marketing, and other teams to provide actionable insights.

• Evaluate and integrate third-party data sources to improve prediction accuracy.

• Stay updated with the latest ML research and adopt cutting-edge methodologies.

Qualifications:

• 8+ years of hands-on experience in machine learning software development.

• Proven expertise in customer lifetime value modeling and related predictive systems.

• Proficiency in XGBoost, deep learning, and end-to-end ML lifecycle management.

• Strong strategic thinking and leadership skills to influence cross-team contributions.

• Excellent communication skills for both technical and non-technical stakeholders.