Environment Scaling

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 Environment Scaling team is a team of researchers and engineers whose goal is to improve the intelligence of our public models for novel verticals and use cases. The team builds the training environments that fuel RL at scale. This role combines executing directly on ML research, data operations, and project management 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

• Improve and execute our fine‑tuning strategies for adapting Claude to new domains and tasks
• 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 environments for high‑value tasks
• Develop and improve QA frameworks to catch reward hacking and ensure environment quality
• Partner with other RL research teams and product teams to translate capability goals into training environments and evals

You May Be a Good Fit If You

• Have experience with fine‑tuning large language models for specific domains or real‑world use cases and/or domain expertise in an area where we would like to make our models more useful
• 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 project management and interpersonal skills
• Are passionate about making AI more useful and accessible across different industries
• Are excited about a role that includes a combination of ML research, data operations, and project management

Strong Candidates May Also

• Have experience training production ML systems
• Be familiar with distributed systems and cloud infrastructure
• Have domain expertise in an area where we would like to make our models more useful
• Have experience working with external vendors or technical partners

Annual Salary

• $350,000 — $850,000 USD

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
• Location‑based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25 % of the time. Some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas, though we cannot guarantee sponsorship for every role and candidate. If we make you an offer, we will make a reasonable effort to obtain a visa and retain an immigration lawyer to assist.

How We’re Different

We believe the highest‑impact AI research will be big science. At Anthropic we work as a single cohesive team on a few large‑scale research efforts and value impact—advancing our long‑term goals of steerable, trustworthy AI—over smaller, isolated puzzles. We view AI research as an empirical science, sharing common ground with physics, biology, and traditional computer science. Collaboration and communication are highly valued; we host frequent research discussions to ensure we pursue the highest‑impact work at any given time.

Benefits

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Collaborative office space in San Francisco (for hybrid staff)
• Guidance on candidates’ AI usage in the application process