Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems
Block, Inc. is a technology company building tools for economic empowerment, operating a portfolio of financial brands including Square, Cash App, Afterpay, TIDAL, Bitkey, and Proto. It serves sellers, consumers, artists, and bitcoin users through payments, banking, buy-now-pay-later, music streaming, and bitcoin self-custody and mining products.
About Block, Inc.
Block, Inc. builds technology aimed at increasing access to the global economy. Its brands each unlock different aspects of the economy: Square makes commerce and financial services accessible to sellers; Cash App is an easy way to spend, send, and store money; Afterpay (Clearpay in the UK) helps customers manage spending over time; TIDAL is a music platform empowering artists as entrepreneurs; Bitkey is a self-custody bitcoin wallet; and Proto builds open, accessible bitcoin mining hardware and services. Block has been a long-time advocate for bitcoin, integrating bitcoin buying/selling and Lightning Network payments into Cash App and Square, funding open-source bitcoin infrastructure through Spiral (including the Lightning Development Kit), and engaging in bitcoin policy advocacy through groups like COPA, the Digital Energy Council, the Crypto Council for Innovation, and the Texas Blockchain Council. The company also runs a significant open source program (contributing to projects like Goose, an on-machine AI developer agent, OkHttp, Retrofit, gRPC, Envoy, and MySQL) and an AI research effort, including co-founding the Agentic AI Foundation with Anthropic and OpenAI. Block serves individual consumers, small and medium-sized businesses/merchants, artists, and developers.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Design and implement production machine learning systems that transform customer behavior, product context, model outputs, and feedback loops into trusted signals for recommendations, ranking, risk-aware decisioning, growth, and customer intelligence.
Requirements
- 12+ years building and operating production software and ML systems for business-critical products.
- Deep expertise in ranking and 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.
- Experience with semantic retrieval, embeddings, two-tower models, graph features, LLM-powered retrieval or decision systems, entity resolution, or real-time personalization is nice to have.
- Experience with experimentation, online evaluation, interleaving, counterfactual evaluation, multi-objective optimization, or long-term holdouts is nice to have.
- Experience building reusable feature or signal platforms, decision services, customer intelligence layers, model-derived data products, or agent-assisted operations is nice to have.
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, 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.
Benefits
- Remote work
- Medical insurance
- Flexible time off
- Retirement savings plans
- Modern family planning