Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems

Block builds technology and financial products designed to increase access to the global economy. The Staff Applied Machine Learning Engineer will build and operate production machine learning systems that transform customer and product context into trusted signals, rankings, recommendations, and decision capabilities. The role partners across product, growth, data, platform, modeling, risk, and compliance to develop measurable, reliable, and customer-impacting ML systems.

Responsibilities:

• Build and operate production ML systems that turn customer and product context into trusted signals, rankings, recommendations, and decision capabilities
• Design production data and signal contracts that define intended use, freshness, provenance, confidence, eligibility, and calibration for downstream consumers
• Own ranking, retrieval, recommendation, search, propensity, and next-best-action systems end to end, from feature and candidate generation through serving, experimentation, monitoring, and feedback loops
• Evaluate customer and business impact beyond short-term conversion, including trust, fairness, access, risk, compliance, long-term engagement, and segment-level performance
• Partner across product, growth, data, platform, modeling, risk, and compliance to translate ambiguous goals into measurable ML system designs
• Use AI and agents to accelerate development, analysis, testing, documentation, and operations while exposing reusable capabilities to product services, internal tools, and AI-assisted workflows

Requirements:

• 12+ years building and operating production software and ML systems for business-critical products
• Deep expertise in intelligent systems such as ranking/retrieval, recommendations, search, personalization, growth and lifecycle ML, customer intelligence, propensity/churn/LTV, next-best-action, or model-derived risk signals
• Strong production ML judgment across feature pipelines, model serving, experimentation, monitoring, feedback loops, online/offline consistency, and reliable signal interfaces
• Ability to evaluate impact beyond short-term conversion, including trust, fairness, access, risk, compliance, and long-term engagement
• Experience using AI-assisted engineering tools with appropriate verification, testing, and review for customer-impacting systems
• We do expect strong production engineering fundamentals, deep domain expertise in intelligent ML systems, and judgment about how ML-derived signals should be used safely in customer-impacting products
• Experience with semantic retrieval, embeddings, two-tower models, graph features, LLM-powered retrieval or decision systems, entity resolution, or real-time personalization
• Experience with experimentation, online evaluation, interleaving, counterfactual evaluation, multi-objective optimization, or long-term holdouts
• Experience building reusable feature/signal platforms, decision services, customer intelligence layers, model-derived data products, or agent-assisted operations