Data Operations Manager, Human Data

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.</p></div><h2 class="heading">About the Role</h2> <p>As Data Operations Manager, you'll build and scale data operations across research teams working on frontier AI capabilities. You'll partner with researchers to design and execute data strategies, manage vendor relationships, and own the entire data pipeline from requirements to production. This role requires operational excellence combined with technical depth to understand what makes high-quality training data, but your focus will be on strategy and execution.</p> <h2 class="heading">About the Impact</h2> <p>The data operations you build will directly determine how well our models perform on critical capabilities—tool use accuracy, prompt injection robustness, long-horizon reasoning, and safety alignment. You'll work with world-class researchers advancing the frontier while building the operational infrastructure to scale these efforts.</p> <p>We're looking for someone who gets excited about the challenge of scaling quality across diverse research areas—someone who can understand nuanced technical requirements, build the right partnerships, and execute flawlessly. If you thrive at the intersection of operational excellence and cutting-edge AI research, we'd love to hear from you.</p> <h2 class="heading">Responsibilities:</h2> <ul> <li> <p>Own and execute data strategy for research teams advancing frontier AI capabilities across RLHF, safety, tool use, and agentic workflows</p> </li> <li> <p>Drive strategic vendor partnerships and build scalable frameworks for technical data collection at scale</p> </li> <li> <p>Design and implement operational systems that translate research requirements into high-quality data pipelines</p> </li> <li> <p>Build evaluation frameworks and quality standards that ensure data meets the bar for training state-of-the-art AI systems</p> </li> <li> <p>Lead cross-functional initiatives to optimize research velocity while maintaining rigorous quality standards</p> </li> <li> <p>Proactively identify risks, bottlenecks, and opportunities to improve efficiency and effectiveness across data operations</p> </li> <li> <p>Partner with senior research leaders to align data operations with model development roadmaps and strategic priorities</p> </li&