Research Engineer, Environment Scaling

Anthropic

4d•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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

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