Senior Machine Learning Engineer, Applied AI
BlockBay Area, CA, United States of AmericaPosted 24 February 2026
Tech Stack
Job Description
<p>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.</p>
<p><strong>The Role</strong></p>
<p>Block is organized into business groups, each with dedicated product and engineering teams. Our team plays a vital role in delivering better-than-human customer support experiences, using AI to make support faster, smarter, and more reliable. By improving how millions of customers get support, we build lasting trust and create meaningful operational leverage across Block.</p>
<p>We are looking for an enthusiastic and experienced Machine Learning Engineer to join our team to build AI customer support products. In this role, you will collaborate closely with teams across Block to understand their challenges and opportunities. You'll design, develop, and launch AI driven features and tools that solve real customer needs, working in an environment that values creativity, experimentation, and practical problem solving.</p>
<p>This is a unique opportunity to shape the future of AI-powered support at Block. You will work on projects that directly impact customers, advocates, and the business, transforming how we serve people and scale trust. We are looking for builders who are passionate about pragmatic AI, motivated by real world impact, and excited to build systems that help people get the help they need.</p>
<p><strong>You Will</strong></p>
<ul>
<li>Leverage AI to automate support workflows and deliver new AI-powered support product experiences</li>
<li>Design, build, and maintain scalable machine learning systems, including developing, fine-tuning, and integrating large language models and related systems, that power real customer and advocate experiences.</li>
<li>Collaborate cross-functionally with product, engineering, design, and operations teams to ship impactful AI features at scale</li>
<li>Build generative AI systems that scale intelligently across multiple business units, adapting to diverse products, users, and use cases</li>
<li>Stay current on the latest developments in LLMs and applied ML, assessing where new techniques (e.g., RAG, distillation, fine-tuning, prompt orchestration) can enhance our systems.</li>
<li>Mentor peers and contribute to technical documentation, design reviews, and shared best practices that raise the engineering bar for applied ML systems.</li>
</ul>
<p><strong>You Have</strong></p>
<ul>
<li>8+ years of experience in training and deploying ML models, developing ML software, and shipping customer-facing ML products.</li>
<li>Deep understanding of modern ML techniques (e.g. Large Language Models) and the ML lifecycle from data gathering, training, model evaluation, MLOps, and productionizing models.</li>
<li>Ruthless pragmatism when solving new and unconventional problems</li>
<li>A desire to understand our clients' needs in order to design tools and systems that solve our customer's problems. As a platform team, our main customers are internal developers and products.</li>
<li>Ability to produce production-quality code and services incorporating testing, evaluation, monitoring as well as the ability to quickly adapt to a new domain, hack MVP's, and iterate to improve product.</li>
<li>Experience using any of the major cloud vendors for high scale production use cases</li>
<li>Strong communication skills (verbal and written) with technical and non-technical stakeholders.</li>
</ul>
<p>We're working to build a more inclusive ... (truncated, view full listing at source)
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