Block
StaffAppliedMachineLearningEngineer-IntelligentData,Signals&Systems
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optimal for Senior candidates.
“Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems at Block. Skills: Applied Machine Learning, Production ML systems, Customer intelligence, Signal systems. Build production ML systems. Operate production ML systems”
What You'll Achieve.
Build production ML systems; Create trusted signals; Design production data contracts; Design signal contracts; Own ranking systems; Own retrieval systems; Own recommendation systems; Own search systems; Own propensity systems; Own next-best-action systems; Own feature generation; Own candidate generation; Own model serving; Own experimentation; Own monitoring; Own feedback loops; Evaluate customer impact; Evaluate business impact; Translate ambiguous goals; Design measurable ML systems; Accelerate development; Accelerate analysis; Accelerate testing; Accelerate documentation; Accelerate operations; Expose reusable capabilities
Industry & Context.
Root cause analysis; Troubleshooting; Data-driven decision making
What They're Looking For.
Must Have
12+ years building software, 12+ years operating ML systems, Production ML judgment, Experience using AI-assisted tools
Nice to Have
Semantic retrieval experience, Embeddings experience, Two-tower models experience, Graph features experience, LLM-powered retrieval experience, LLM-powered decision systems experience, Entity resolution experience, Real-time personalization experience, Experimentation experience, Online evaluation experience, Interleaving experience, Counterfactual evaluation experience, Multi-objective optimization experience, Long-term holdouts experience, Reusable feature platforms experience, Reusable signal platforms experience, Decision services experience, Customer intelligence layers experience, Model-derived data products experience, Agent-assisted operations experience
What You'll Do.
Build production ML systems
Operate production ML systems
Transform customer behavior
Transform product context
Transform model outputs
Transform feedback loops
Create trusted signals
Design production data contracts
Design signal contracts
Own retrieval systems
Own recommendation systems
Own propensity systems
Own next-best-action systems
Own feature generation
Own candidate generation
Evaluate customer impact
Evaluate business impact
Evaluate long-term engagement
Evaluate segment-level performance
Partner across product
Partner across growth
Partner across platform
Partner across modeling
Partner across compliance
Translate ambiguous goals
Design measurable ML systems
Accelerate development
Accelerate documentation
Accelerate operations
Expose reusable capabilities
How You'll Work.
Team & Collaboration
Product teams; Growth teams; Data teams; Platform teams; Modeling teams; Risk teams; Compliance teams; Product surfaces; Decision engines; Internal tools; AI-assisted workflows
Full Job Description
Block builds simple, powerful tools that make progress towards an economy that’s truly open to all. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we’re helping build a financial system that is open to everyone. Join us. The Role 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,
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