Software Engineer, Human Data Interface

About Anthropic 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. About the Role Anthropic's Human Data Interfaces team builds the systems that collect data to improve our models. This includes novel interfaces for data vendors, tooling, and front-end and back-end infrastructure that enables researchers to gather high-quality data at scale. As a Software Engineer, you'll own the architecture and execution of our data collection pipelines — designing systems that are both performant at scale and resilient to the rapidly changing needs of our research teams. You'll work closely with researchers, our cross-functional data operations partners, and the crowdworkers and vendors who use these tools day-to-day. Responsibilities: - Architect and build data collection pipelines that support rapid iteration, balancing data quality and system maintainability - Think deeply about the experience of the crowdworkers and vendors using these systems, building interfaces that are clear, efficient, and lead to high-quality data - Collaborate closely with research teams to understand evolving data needs and iterate quickly on collection methods - Partner with our Human Data Operations team to understand the end-to-end data workflow and design interfaces that make their jobs easier - Prioritize and juggle multiple workstreams, making trade-off decisions in a fast-moving environment where research priorities can shift quickly You May Be a Good Fit If You: - Believe that advanced AI systems could have a transformative effect on the world and are interested in helping make sure that transformation goes well - Are a strong full-stack engineer with broad experience across the stack - Are very good at building internal tools, including working with users of the tools to understand their needs - Thrive in fast-moving environments where you need to balance speed of iteration with long-term system health - Are a quick study—this team sits at the intersection of a large number of different complex technical systems that you'll need to understand (at a high level) to be effective Strong Candidates May Also Have: - Experience building human data labelling interfaces, human-in-the-loop systems, or data collection pipelines - Familiarity with how preference data and reward models are used in AI model training - Experience working with researchers who are internal users/customers - Background in building, and improving the user-experience of user-facing applications, particularly those involving complex UI interactions or annotation workflows - Strong instincts around system design — building things that evolve gracefully as requirements change - Experience influencing technical and product direction on a team The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000—$405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from

You may be a good fit if you are a strong full-stack engineer with broad experience across the stack, are very good at building internal tools (including working with users of the tools to understand their needs), thrive in fast-moving environments where you balance speed of iteration with long-term system health, and are a quick study who can understand a wide range of complex technical systems. Strong candidates may also have experience building human data labelling interfaces, human-in-the-loop systems, or data collection pipelines; familiarity with how preference data and reward models are used in AI model training; experience working with researchers who are internal users/customers; background in building and improving user-facing applications with complex UI interactions or annotation workflows; strong instincts around system design; and experience influencing technical and product direction on a team. Minimum education: Bachelor's degree or equivalent; required field of study: a field relevant to the role.

Architect and build data collection pipelines that support rapid iteration, balancing data quality and system maintainability. Think deeply about the experience of the crowdworkers and vendors using these systems, building interfaces that are clear, efficient, and lead to high-quality data. Collaborate closely with research teams to understand evolving data needs and iterate quickly on collection methods. Partner with the Human Data Operations team to understand the end-to-end data workflow and design interfaces that make their jobs easier. Prioritize and juggle multiple workstreams, making trade-off decisions in a fast-moving environment where research priorities can shift quickly.