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

As a Staff Applied Machine Learning Engineer focused on Intelligent Data, Signals & Systems, you will build production ML systems that transform customer behavior, product context, model outputs, and feedback loops into trusted signals used by recommendations, ranking, risk-aware decisioning, growth, and customer intelligence systems.

This role centers on customer intelligence and reusable model-derived signal systems: ranking and retrieval, recommendations, search, propensity and churn/LTV, next-best-action decisioning, experimentation, and feedback loops. These systems help product, growth, fraud, and risk teams make better decisions with clear freshness, provenance, confidence, and evaluation guarantees.

The work combines production ML systems with composable signal interfaces that can be consumed by product surfaces, decision engines, internal tools, and verified AI-assisted workflows. The role is flexible across Applied ML Engineering domains while still requiring deep expertise.

You Will

• 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., We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws.

• 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.

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., We do not expect candidates to have used our exact stack. 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. Examples of technologies and methods include:
• Python, Java, Kotlin, SQL.
• TensorFlow, PyTorch, XGBoost/LightGBM, ranking/retrieval systems, embeddings, semantic search, recommendation frameworks.
• Event streams, batch pipelines, feature stores, model-serving infrastructure, workflow orchestration, experimentation systems, and data warehouses/lakehouses.
• Cloud infrastructure, Kubernetes, observability tooling, coding agents, evaluation harnesses, and agent-assisted operations tooling.

Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering. _Check out our other benefits at Block.