This role pays more than
99%
of similar roles. Most pay
$184,787–$259,421
— the shaded band above.
At the midpoint, this role pays about
$346k
versus about
$222k
for comparable roles.
Based on 240 similar postings.
Employer
About Block
Block, Inc. (formerly Square) is a financial technology company operating the Square merchant payments ecosystem, Cash App peer-to-peer payments, TIDAL music streaming, and Bitcoin-focused financial services. Industry: Financial Technology & Payments
Block currently has
138 open roles
on FindRole.
Listed pay typically runs
$180,000–$270,000
across 61 roles with salary data.
As a Staff Applied Machine Learning Engineer in the Intelligent Data, Signals & Systems team, you will develop and maintain production ML systems that convert customer behavior and product context into actionable signals for recommendations, ranking, risk management, growth strategies, and more. Your daily tasks include designing data contracts to ensure signal quality and reliability, owning end-to-end systems from feature generation through feedback loops, and collaborating with cross-functional teams to translate business goals into effective ML solutions. You will leverage technologies such as Python, TensorFlow, PyTorch, and cloud infrastructure to build robust, scalable systems that enhance customer intelligence and decision-making capabilities across various domains like ranking, retrieval, and personalization. This role demands deep expertise in intelligent systems and strong judgment in deploying ML-derived signals safely in business-critical applications.
Build and operate ML systems that convert customer and product context into trusted signals.
Design data contracts defining use, freshness, provenance for downstream consumer systems.
Own end-to-end development of ranking, recommendation, search, and decisioning systems.
Evaluate long-term business impact including trust, fairness, risk, compliance, engagement.
Partner with cross-functional teams to translate goals into measurable ML system designs.
What we're looking for
12+ years of experience building and operating production ML systems for business-critical products.
Deep expertise in intelligent systems like ranking/retrieval, recommendations, search, personalization, growth, customer intelligence, propensity/churn/LTV, next-best-action, or model-derived risk signals.
Strong judgment in production ML across feature pipelines, serving, experimentation, monitoring, feedback loops, and reliable signal interfaces.
Ability to evaluate long-term impact on trust, fairness, access, risk, compliance, engagement, and segment performance.
Experience using AI-assisted engineering tools with proper verification for customer-impacting systems.