Anthropic is recruiting a Research Engineer, Domain Scaling to help advance the capabilities of Claude across important real-world knowledge-work domains. The position is designed for an experienced artificial intelligence and machine learning professional interested in combining applied research, reinforcement learning, data strategy and hands-on experimentation.
The successful candidate will join Anthropic’s Domain Scaling team, which focuses on making Claude highly capable in areas such as finance, healthcare and legal services. The role offers the opportunity to work at the intersection of AI research, data sourcing, reinforcement learning environments and model evaluation.
Research Engineer Job Details
- Position: Research Engineer, Domain Scaling
- Organisation: Anthropic
- Locations: San Francisco, California; New York City, New York; or Seattle, Washington
- Employment: Full-time
- Annual Salary: USD $350,000–$850,000
- Minimum Education: Bachelor’s degree or equivalent combination of education, training and experience
- Work Model: Hybrid
- Office Requirement: At least 25% of working time in an Anthropic office
- Visa Sponsorship: Available, subject to role and candidate eligibility
Anthropic notes that experience requirements correspond to the internal job level associated with the position. Candidates do not need to meet every listed qualification and are encouraged to apply if they have relevant expertise and believe they can contribute to the work.
About Anthropic
Anthropic is an artificial intelligence company focused on developing reliable, interpretable and steerable AI systems. Its research and engineering teams work across AI safety, model development and practical applications with the objective of building AI systems that are beneficial to users and society.
The Research Engineer, Domain Scaling will contribute to this mission by helping improve AI performance on complex knowledge-work tasks across multiple industries.
Role Overview
The Domain Scaling team aims to make Claude more effective at professional and industry-specific work. The Research Engineer will help develop the data and reinforcement learning infrastructure required to train and evaluate models for high-value tasks.
The role combines applied AI research and data strategy. The successful candidate will be responsible for identifying valuable tasks, sourcing real-world and synthetic data, designing reward signals, developing reinforcement learning environments and measuring the effect of new strategies on model capabilities.
This makes the position particularly suitable for professionals who enjoy moving between research, experimentation, data analysis and practical implementation.
Key Responsibilities
The Research Engineer will be expected to:
- Own data strategies for knowledge-work verticals from task sourcing through reinforcement learning training.
- Manage technical relationships with external data vendors.
- Evaluate data quality and contribute to reward-design processes.
- Collaborate with domain experts to develop data pipelines and evaluation systems.
- Explore innovative approaches for creating reinforcement learning environments.
- Build and improve quality-assurance frameworks to identify reward hacking and maintain environment quality.
- Conduct generalisation experiments to determine how data strategies affect model capabilities.
- Work with reinforcement learning research teams and product teams to translate capability objectives into training environments and evaluations.
The position therefore requires both technical depth and the ability to collaborate effectively across research, engineering, product and external partner teams.
Candidate Eligibility and Requirements
Strong candidates are expected to have experience with large language models, reinforcement learning or LLM training data. Relevant experience may include fine-tuning large language models for specific domains or real-world applications.
Applicants should ideally demonstrate experience in one or more of the following areas:
- Fine-tuning large language models.
- Reinforcement learning and reward design.
- LLM training data curation.
- Machine learning research and experimentation.
- Technical data sourcing and evaluation.
- Data quality assurance.
- Cross-functional collaboration.
- Technical vendor or partner management.
Candidates should be comfortable reviewing datasets directly, identifying quality issues and rapidly incorporating feedback into research and development processes.
Additional Experience That Can Strengthen an Application
Anthropic identifies several backgrounds that may distinguish particularly strong applicants. These include experience training production machine learning systems, designing LLM evaluations or benchmarks, and possessing domain expertise in an industry where advanced AI capabilities could provide significant value.
Experience working with external technical vendors, research partners or other organisations can also be advantageous.
Professionals who combine AI research knowledge with practical experience in data strategy, evaluation and model training may be especially well positioned for the role.
Education and Work Location
The minimum education requirement is a Bachelor’s degree or equivalent combination of education, training and professional experience in a field relevant to the position.
Anthropic currently operates a location-based hybrid policy requiring staff to work from one of its offices at least 25% of the time, although individual roles may require greater in-office participation.
The position is available in San Francisco, New York City and Seattle, making candidates who can work from one of these locations particularly relevant.
Salary, Visa Sponsorship and Benefits
The advertised annual compensation range is USD $350,000 to $850,000. The final compensation level can vary according to the position’s internal level and the candidate’s qualifications and experience.
Anthropic also states that it sponsors visas for eligible roles and candidates. While sponsorship is not guaranteed for every position, the company indicates that it makes reasonable efforts to support successful candidates through the immigration process.
Employees may receive competitive benefits, equity donation matching, generous vacation and parental leave, flexible working arrangements and access to collaborative office environments.
Application Information
Interested candidates can apply through Anthropic’s official careers platform. Applicants should be prepared to provide a resume or LinkedIn profile, with at least one required as part of the application.
The application also asks candidates about their motivation for joining Anthropic, willingness to work in an office at least 25% of the time, relocation, visa sponsorship and potential start dates.
Anthropic encourages candidates to review its AI partnership guidelines before completing the application. The company states that AI tools may be used thoughtfully during the application process, while candidates are expected to demonstrate their own skills, expertise and perspective.
Why This AI Research Opportunity Stands Out
The Research Engineer, Domain Scaling position offers an opportunity to work on advanced LLM training, reinforcement learning environments, data strategy and AI evaluations while addressing practical challenges in professional knowledge work.
For experienced AI and machine learning professionals, the role provides a combination of high-level research and hands-on engineering and data work. The position is particularly relevant to candidates interested in improving the usefulness of AI across sectors such as finance, healthcare and law while contributing to Anthropic’s broader goal of developing safe, reliable and steerable AI systems.
