Research Engineer, Domain Scaling

Menlo Ventures; New York (NY); Hybrid; USD 350,000 - 850,000; Full time

Benefits offered by this job

Generous vacation and parental leave

Flexible working hours

Lovely office space

Job summary

Menlo Ventures is seeking an individual for a unique role in their Domain Scaling team focused on improving AI systems for real-world applications. You will own the end-to-end data strategy for knowledge work, manage relationships with external data vendors, and collaborate closely with domain experts. This position requires a Bachelor’s degree and experience in fine-tuning language models, reinforcement learning, and vendor management.

The compensation ranges from $350,000 to $850,000 annually with competitive benefits and a flexible hybrid working policy.

Qualifications

• Experience with fine-tuning large language models for real-world use cases.
• Comfortable managing technical vendor relationships.
• Strong cross-functional collaboration skills.

Responsibilities

• Own the data strategy for knowledge work verticals end‑to‑end.
• Manage technical relationships with external data vendors.
• Collaborate with domain experts to design data pipelines.

Skills

Fine-tuning large language models

Reinforcement learning

Reward design

Technical vendor relationship management

Cross-functional collaboration

Education

Bachelor’s degree or equivalent

Job description

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

• 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

Qualifications

• 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

Preferred

• 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

Compensation

Annual Salary: $350,000—$850,000 USD

Logistics

Minimum education: Bachelor’s degree or 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.

Benefits

Anthropic offers 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.