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.
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.
ResponsibilitiesOwn the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training
Manage technical relationships with external data vendors, including evaluation of data quality and reward design
Collaborate with domain experts to design data pipelines and evaluations
Explore novel ways of creating RL envs for high value tasks
Develop and improve QA frameworks to catch reward hacking and ensure env quality
Run generalization experiments to measure how data strategy changes improve model capabilities
Partner with other RL research teams and product teams to translate capability goals into training envs and evals
Have experience with fine-tuning large language models for specific domains or real-world use cases
Have experience with reinforcement learning, reward design, or training data curation for LLMs
Are comfortable managing technical vendor relationships and iterating quickly on feedback
Find value in reading through datasets to understand them and spot issues
Have strong cross-functional collaboration skills
Are passionate about making AI more useful and accessible across different industries
Are excited about a role that includes a combination of applied research and hands-on data work
Have experience training production ML systems
Have experience designing evals or benchmarks for LLMs
Have domain expertise in a vertical where we would like to make our models more useful
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.
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 @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Skills Required
- Bachelor's degree or equivalent combination of education, training, and/or experience
- Field of study relevant to the role (demonstrated via coursework, training, or experience)
- Experience fine-tuning large language models for specific domains or real-world use cases
- Experience with reinforcement learning, reward design, or training data curation for LLMs
- Experience managing technical vendor relationships and evaluating data quality
- Ability to read and analyze datasets to identify issues and data quality problems
- Strong cross-functional collaboration skills
- Comfort with applied research combined with hands-on data work
- Experience training production ML systems
- Experience designing evaluations or benchmarks for LLMs
- Domain expertise in a vertical (e.g., finance, healthcare, legal)
- Experience working with external vendors or technical partners
Anthropic Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Anthropic and has not been reviewed or approved by Anthropic.
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Strong & Reliable Incentives — Pay is positioned as top-of-market for many technical roles through a mix of high base pay, equity, and occasional bonuses/signing incentives. Benefits like substantial monthly stipends and employer-paid protections further strengthen perceived total rewards.
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Healthcare Strength — Healthcare is described as comprehensive across medical, dental, and vision, with additional mental-health support. Coverage is framed as robust for employees and dependents, which can materially increase the value of the overall package.
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Parental & Family Support — Paid parental leave is described as notably generous, alongside fertility coverage and other family-oriented supports. These elements broaden the rewards package beyond cash compensation and can improve retention for caregivers.
Anthropic Insights
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
