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

Block•Bay Area, CA, United States of America, CA

8h•$276,800 - $415,200•Remote

About The Position

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.

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.

Nice To Haves

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.

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.