Staff Machine Learning Engineer (Modeling), Support

Seattle, WA · US - CA - Bay Area - Remote · USD 276,800-415,200 · 10+ years experience · Remote · Posted Sep 22, 2026

At a glance

Block's Support ML Modeling team is a central driver of innovation in customer support experiences across the entire ecosystem, including Cash App and Square.
The team is dedicated to advancing the state of intelligent, automated support through machine learning and generative AI.
The role involves building high-impact, scalable systems that improve support quality, efficiency, and accessibility from chatbots to internal tools.

Lead the end-to-end delivery of multiple ML initiatives, from planning and design through rollout, documentation, and long-term maintenance
Partner strategically with risk, product, engineering, design, and operations leaders to define and drive long term ML roadmaps, informed by domain expertise and industry trends
Guide the team’s direction, identifying new ML/AI opportunities and advising leadership on strategic tradeoffs and opportunities
Drive R&D efforts exploring next-generation chatbot architectures using LLMs, RAG, fine-tuning, and real-time inference
Design, deploy, and maintain ML models powering conversational agents including support chatbots across Cash App, Square, and other Block products
Develop ML-powered tools and real time recommendation systems that enhance support agent effectiveness and customer outcomes
Act as the technical representative for your team’s systems and programs, clearly communicating work and results to stakeholders, cross-functional partners, and external audiences

10+ years of experience in machine learning, applied AI, or product ML roles, with deep technical expertise
Demonstrated leadership capabilities, with the ability to influence and align cross-functional teams while directly shaping the work of peers through communication, context sharing, and technical guidance
A track record of delivering organizational wide impact, shaping systems, frameworks, or initiatives that raise the bar across multiple teams or functions
Proven ability to ship end to end ML features from problem framing through deployment and long-term maintenance
Demonstrated experience with language models, dialog systems, or generative AI in production
Strong foundation in NLP, deep learning, or ML infrastructure best practices
Excellent communication skills, with the ability to represent the team’s work to leadership and stakeholders
Enthusiasm for R&D and pushing the boundaries of applied AI to transform conversational systems