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

Block builds simple, powerful tools that make progress towards an economy that’s truly open to all. As a Staff Applied Machine Learning Engineer focused on Intelligent Data, Signals & Systems, you will build production ML systems that transform customer behavior into trusted signals used by various teams for decision-making. The role requires deep expertise in intelligent systems and involves collaboration across multiple domains to create effective machine learning solutions.

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
• 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