• Design, build, and operate greenfield SIP trunking infrastructure connecting carrier-grade telephony to our internal voice AI platform. • Architect and implement real-time bidirectional audio streaming pipelines to support our ECS-based voice stack. • Design autoscaling strategies for stateful, connection-based workloads, enabling the platform to scale dynamically based on connection ingress and concurrent call volume. • Implement call routing, inbound/outbound dialing flows, and media transport (RTP/SRTP). • Integrate with Amazon Connect to bridge carrier connectivity into our custom infrastructure. • Collaborate with ML engineers, product managers, and other stakeholders to ensure the telephony layer meets the needs of our voice AI systems. • Ensure outbound dialing infrastructure complies with TCPA regulations and supports STIR/SHAKEN attestation.

Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams - People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more - provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block. Block's Support ML Modeling team is a central driver of innovation in customer support experiences across our entire ecosystem-including Cash App, Square, and other business units. We are dedicated to advancing the state of intelligent, automated support through machine learning and generative AI. From customer-facing chatbots to smart internal tools for agents, our team builds high-impact, scalable systems that improve support quality, efficiency, and accessibility. We're building the future of support at Block: one powered by AI, voice interfaces, and smart automation. We're looking for candidates with a passion for intelligent systems, practical ML experience, and a desire to build product-driven solutions. You Will Lead R&D efforts to explore and prototype next-generation chatbot architectures using LLMs, retrieval-augmented generation (RAG), fine-tuning, and real-time inference Design and deploy ML models powering conversational agents, including the support chatbot used across Cash App, Square, and other Block products Build generative AI systems that scale intelligently across multiple business units, adapting to diverse products, users, and use cases Advance voice support automation, enabling natural, responsive, and accurate voice-based interactions Develop systems to infer customer intent, enabling effective routing, triaging, and resolution of cases with minimal human involvement Create ML-powered tooling and real-time recommendation systems to assist support agents and enhance customer outcomes Engineer robust, reusable modeling pipelines capable of high throughput, rapid iteration, and easy deployment Collaborate cross-functionally with product, engineering, design, and operations teams to ship impactful ML features at scale You Have 6+ years of experience in machine learning, applied AI, or product ML roles Demonstrated experience with language models, dialog systems, or generative AI in production Strong knowledge of NLP, deep learning, and ML infrastructure best practices Experience with speech processing or voice interfaces is a strong plus Proven ability to ship end-to-end ML features-framing problems, prototyping, training, evaluation, and deployment Experience designing scalable model pipelines and maintaining production ML services Excellent communication skills and a collaborative mindset Enthusiasm for R&D and pushing the boundaries of what AI can do for support Technologies We Use and Teach Python, PyTorch, TensorFlow, or JAX 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 also consider qualified applicants with criminal histories for employment on our team, and always assess candidates on an individualized basis. 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. Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page . Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.

The Role As a Staff Machine Learning Engineer in Fraud and Abuse ML Engineering, you will play a central role in designing, building, and evolving the machine learning systems that safeguard Block's ecosystem from fraud, abuse, and other malicious activity. You will architect and lead the development of high-scale, real-time ML systems that power our fraud decisioning across the Block network, protecting millions of customers and merchants while maintaining seamless user experiences. You Will Build and maintain real-time and batch data pipelines/APIs to support ML model inference at scale. Design elegant ML pipelines and services, prototype new approaches, and productionize solutions at scale. Collaborate with product and engineering teams to define data models and schemas for consistent and structured data flow. Integrate and enrich diverse internal and third-party data sources to enhance our feature store and modeling capabilities. Ensure data quality and completeness through automated validation, monitoring, and alerting. Develop new triggers and event hooks that support enhanced risk evaluations and detections. Participate in SEV management by rapidly integrating new data, deploying new features, and implementing stopgap controls to mitigate risk. Apply ML and engineering best practices to shape how Block develops, tests, and maintains ML-platform solutions. Support the integration of risk decisions and scores into downstream systems, ensuring they are consumed and acted upon correctly. You Have 12+ years of experience in software development and demonstrated technical initiative and leadership on previous machine learning projects. A proven ability to shape how teams effectively adopt and evolve AI practices; driving adoption of AI-first workflows by coaching leaders, identifying scalable use cases, and embedding quality and accountability in team norms. Experience leading experimentation cycles and building a shared understanding of how AI drives business outcomes. Curiosity and a passion for Block's mission of economic empowerment. Experience helping your teammates grow through mentorship and providing constructive feedback. A desire to be hands-on and help invent the future of AI. The ability to work autonomously in the rapidly evolving world of ML. Technologies We Use and Teach Python, Java and Kotlin Tensorflow and PyTorch AWS, Databricks and Kubernetes MySQL, DynamoDB, Kafka, Spark 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 a proud equal opportunity employer. We work hard to evaluate all employees and job applicants consistently, based solely on the core competencies required of the role at hand, and without regard to 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. Want to learn more about what we're doing to build an inclusive workplace? Check out our Inclusion & Diversity page Full-time employee benefits include the following: Healthcare coverage (Medical, Vision and Dental insurance) Health Savings Account and Flexible Spending Account Retirement Plans including company match Employee Stock Purchase Program Wellness programs, including access to mental health, 1:1 financial planners, and a monthly wellness allowance Paid parental and caregiving leave Paid time off (including 12 paid holidays) Paid sick leave (1 hour per 26 hours worked (max 80 hours per calendar year to the extent legally permissible) for non-exempt employees and covered by our Flexible Time Off policy for exempt employees) Learning and Development resources Paid Life insurance, AD&D, and disability benefits These benefits are further detailed in Block's policies. This role is also eligible to participate in Block's equity plan subject to the terms of the applicable plans and policies, and may be eligible for a sign-on bonus. Sales roles may be eligible to participate in a commission plan subject to the terms of the applicable plans and policies. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans. Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.

It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world's relationship with money to make it more relatable, instantly available, and universally accessible. Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We've been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy. The Role Block has provided over $200 billion in credit to customers globally. Afterpay and Cash App Borrow are our two largest products in this space, expanding access to credit for consumers who are often underserved by traditional financial systems. Machine learning is the core of how these products work. Our models decide who gets credit, how much, and under what terms. They underwrite customers across a wide range of credit profiles, including many with thin or no traditional credit history. The modeling challenges are real: maintaining calibration across diverse borrower populations, designing features that generalize as the portfolio grows, and balancing approval rates against loss performance at every decision point. This requires strong fundamentals, disciplined experimentation, and continuous evaluation in production. On the Credit Modeling team, you will be a senior individual contributor building and evolving the ML systems behind these products. You will work across the full modeling lifecycle: problem formulation, feature development, training, calibration, experimentation, deployment, monitoring, and iteration. You will operate across two distinct lending products with different borrower populations, repayment structures, and regulatory surfaces. We use agentic engineering and AI tooling to build reliable, high-velocity workflows that enable this work. That includes code generation, automated testing, documentation, and developer tooling. You will help define how these practices scale across the team in ways that are rigorous, auditable, and trusted. This is a team that values high output and rigor. We move fast, we test carefully, and we hold our work to a high standard because the models we build determine real credit outcomes for real people. This role is fully remote for candidates based in the US or Canada. You Will Build, evaluate, and maintain underwriting and decisioning models across Cash App Borrow and Afterpay. Design and evolve credit decision frameworks, including the modeling, automation, and policy logic that manage credit exposure over time. Design and run experiments to evaluate model performance, measure impact on approval rates and loss, and inform credit policy decisions. Develop deep understanding of borrower behavior, repayment dynamics, and portfolio structure across both products, and use that to inform model design and decision logic. Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders. Work across the full modeling lifecycle: problem formulation, feature engineering, training, calibration, deployment, monitoring, and iteration in production. Build agentic engineering workflows that accelerate development, testing, and documentation. Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure credit systems reflect business goals and regulatory expectations. Share modeling context and approaches across teams, helping align how credit risk is measured, interpreted, and discussed. Shape how AI developer tooling is adopted across the team, defining review practices, quality standards, and governance patterns. You Have A Bachelor's degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science). Advanced degrees welcome. 10+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains. Experience with probabilistic models and decision systems, including calibration, score transformations, and interpretation of model outputs. Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics. Experience with model monitoring, degradation detection, and retraining strategies in production systems. Proficiency with AI-native development workflows. You use LLMs, agentic coding tools, and AI-assisted automation as a regular part of how you build and ship. Experience explaining modeling concepts, results, and limitations to senior stakeholders and cross-functional partners. Experience working across disciplines in environments with meaningful constraints. Technologies We Use and Teach Python (NumPy, Pandas, scikit-learn, PyTorch, XGBoost, LightGBM) AI development tools as core infrastructure: Claude Code, Cursor, Copilot MLflow for experiment tracking and model registry Internal feature store and model hosting platform Prefect and Airflow for orchestration SQL / Snowflake GitHub GCP / AWS Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.