Research Engineer, Domain Scaling

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., * Own 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

Do you have experience in Vendor relationship building?, Do you have a Bachelor’s degree?, * 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

Strong candidates may also

• 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, 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.

Pulled from the full job description

• Parental leave
• Flexible schedule, 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.

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., 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.