Machine Learning Modeler, Advanced Insights & Modeling

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.

Team Overview

The Advanced Insights & Modeling (AIM) team at Block transforms strategic decision-making by bridging the gap between business intuition and data-driven intelligence. We design and deploy models that forecast financial outcomes, estimate customer value, optimize go-to-market (GTM) strategies, and automate planning workflows—helping leaders across the company make faster, smarter, and more confident decisions.

The Role

We're looking for a Machine Learning Engineer (L5) to design and scale the next generation of intelligent systems that power data-driven decision-making across Block—from Cash App and Square to Corporate domains like Treasury, Cost, and Accounting.

You'll build models and AI-driven workflows that don't just predict outcomes—they help shape them. Working across the full ML lifecycle, you'll transform raw data into foresight through advanced modeling, agentic AI workflows, and automation frameworks that enable faster, smarter decisions at scale.

You'll partner closely with analytics and data science teams to bring experimental models into production and with finance and operations partners to build explainable, self-optimizing systems that make forecasts and insights transparent, actionable, and continuously learning.

You Will

• Design and implement forecasting, financial, or optimization models that power strategic decisions across Block.
• Build end-to-end ML pipelines for training, deployment, and monitoring, ensuring reproducibility and performance at scale.
• Collaborate with Data Science to productionize experimental models and integrate them into live systems.
• Partner with Analytics & Finance teams to ensure forecasts are interpretable, accurate, and aligned with business objectives.
• Develop or contribute to explainability tools that communicate model drivers, confidence, and uncertainty to stakeholders.
• Improve data pipelines and workflows using systems like Airflow, BigQuery, and Spark.
• Establish and document best practices for model evaluation, experimentation, and maintenance.
• Translate complex technical findings into clear, actionable recommendations for non-technical partners.
• Contribute to a culture of curiosity, high-quality engineering, and continuous learning within the AIM organization.

You Have

• 5+ years of experience in software or ML engineering, with hands-on experience delivering production-grade ML systems.
• Deep understanding of applied ML and forecasting, including time-series, regression, and value prediction modeling.
• Strong proficiency in Python and common ML libraries such as scikit-learn, XGBoost, LightGBM, and NumPy/pandas.
• Experience building data pipelines using tools such as Airflow, Spark, or similar orchestration systems, and working with BigQuery or other large-scale data warehouses.
• Familiarity with model explainability techniques (e.g., SHAP, feature attribution, uncertainty quantification).
• Excellent analytical and communication skills; able to connect model design to business objectives.
• Proven ability to work cross-functionally and drive high-impact results in fast-paced environments.
• Experience in forecasting or planning models in fintech, consumer, or marketplace settings.
• Exposure to automated model serving, monitoring, or feedback loops in production.
• Background in statistical modeling, uncertainty estimation, or model interpretability research.