Block is seeking a Staff Applied Machine Learning Engineer to build production ML systems that transform customer behavior and model outputs into trusted signals for recommendations, ranking, and decisioning. This role focuses on customer intelligence and reusable ML systems, including ranking, retrieval, recommendations, propensity, and next-best-action, working across product, growth, and risk teams.
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 defining intended use, freshness, provenance, confidence, eligibility, and calibration.
Own ranking, retrieval, recommendation, search, propensity, and next-best-action systems end-to-end, from feature generation through serving and monitoring.
Evaluate customer and business impact beyond short-term conversion, including trust, fairness, access, risk, compliance, and long-term engagement.
Partner with product, growth, data, platform, risk, and compliance teams 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.
Requirements
12+ years building and operating production software and ML systems for business-critical products.
Block, Inc. is a financial services and technology company that provides a connected ecosystem of tools for consumers and merchants, including point-of-sale systems, digital wallets, and financial platforms. Its portfolio includes widely used brands such as Square, Cash App, Afterpay, and Tidal.
Deep expertise in intelligent systems such as ranking/retrieval, recommendations, search, personalization, growth/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, 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.
Nice to Have
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.