Block is hiring a Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems in 10109 Risk - Risk ML & DS — Bay Area, CA, United States of America. The overview below is synthesized from the employer posting on block.xyz: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
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.
The Role
Day-to-day scope for the Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems as described in the posting:
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.
You Have
- 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
Additional experience noted as a plus:
- 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.
Technologies We Use and Teach
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.
- 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.
We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class.
We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible.
While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate.
Block takes a market-based approach to pay, and pay may vary depending on your location. U.S.
To find a location’s zone designation, please refer to this resource. If a location of interest is not listed, please speak with a recruiter for additional information.
Zone A:
$228,700—$343,100 USD
Zone B:
$217,300—$325,900 USD
Zone C:
$205,900—$308,900 USD
Zone D:
$194,500—$291,700 USD
Application Guidelines
Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed.
Use of AI in Our Hiring Process
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.
Contact us here with hiring practice or data usage questions.
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.
Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy.