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

Block Block · Fintech · CA · Remote · 10409 Engineering - AIDA

Staff Applied Machine Learning Engineer to build production ML systems for customer intelligence, signals, ranking, recommendations, search, and decisioning. The role involves designing data and signal contracts, owning systems end-to-end, evaluating business impact beyond conversion, and partnering with various teams. It also emphasizes using AI and agents to accelerate development and operations, and exposing reusable capabilities. Requires deep expertise in intelligent systems and production ML judgment.

What you'd actually do

  1. Build and operate production ML systems that turn customer and product context into trusted signals, rankings, recommendations, and decision capabilities.
  2. Design production data and signal contracts that define intended use, freshness, provenance, confidence, eligibility, and calibration for downstream consumers.
  3. 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.
  4. Evaluate customer and business impact beyond short-term conversion, including trust, fairness, access, risk, compliance, long-term engagement, and segment-level performance.
  5. Partner across product, growth, data, platform, modeling, risk, and compliance to translate ambiguous goals into measurable ML system designs.
  6. 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.

Skills

Required

  • building and operating production software and ML systems
  • intelligent systems (ranking/retrieval, recommendations, search, personalization, growth and lifecycle ML, customer intelligence, propensity/churn/LTV, next-best-action, or model-derived risk signals)
  • production ML judgment (feature pipelines, model serving, experimentation, monitoring, feedback loops, online/offline consistency, reliable signal interfaces)
  • evaluating impact beyond short-term conversion (trust, fairness, access, risk, compliance, long-term engagement)

Nice to have

  • semantic retrieval
  • embeddings
  • two-tower models
  • graph features
  • LLM-powered retrieval or decision systems
  • entity resolution
  • real-time personalization
  • experimentation
  • online evaluation
  • interleaving
  • counterfactual evaluation
  • multi-objective optimization
  • long-term holdouts
  • reusable feature/signal platforms
  • decision services
  • customer intelligence layers
  • model-derived data products
  • agent-assisted operations

What the JD emphasized

  • 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.

Other signals

  • production ML systems
  • customer intelligence
  • reusable signal systems
  • ranking and retrieval
  • recommendations
  • search
  • decisioning
  • experimentation
  • feedback loops
  • AI and agents to accelerate development