Research Engineer, Environment Scaling at Anthropic — Remote North America

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 is a unique role that 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

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.