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
The Domain Scaling team has the goal to make Claude world-class at real-world knowledge work in domains like finance, healthcare, and legal. This is a unique role that combines executing directly on applied research and data sourcing (real-world and synthetic) to improve our models. You'll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance.
Responsibilities
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Own the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training
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Manage technical relationships with external data vendors, including evaluation of data quality and reward design
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Collaborate with domain experts to design data pipelines and evaluations
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Explore novel ways of creating RL envs for high value tasks
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Develop and improve QA frameworks to catch reward hacking and ensure env quality
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Run generalization experiments to measure how data strategy changes improve model capabilities
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Partner with other RL research teams and product teams to translate capability goals into training envs and evals
You may be a good fit if you
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Have experience with fine-tuning large language models for specific domains or real-world use cases
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Have experience with reinforcement learning, reward design, or training data curation for LLMs
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Are comfortable managing technical vendor relationships and iterating quickly on feedback
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Find value in reading through datasets to understand them and spot issues
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Have strong cross-functional collaboration skills
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Are passionate about making AI more useful and accessible across different industries
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Are excited about a role that includes a combination of applied research and hands-on data work
Strong candidates may also
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Have experience training production ML systems
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Have experience designing evals or benchmarks for LLMs
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Have domain expertise in a vertical where we would like to make our models more useful
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Have experience working with external vendors or technical partners
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.
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
Loca…
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