Senior Machine Learning Engineer; Modeling

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.

• 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

• 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

• Python, PyTorch, Tensor Flow, or JAX

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.

Zone A $198,000—$297,000 USD

Zone B $188,100—$282,100 USD

Zone C $178,200—$267,400 USD

Zone D $168,300—$252,500 USD

Block will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances. 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…