Tech Stack

Description

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

• Experience with fine-tuning large language models for specific domains or real-world use cases
• Experience with reinforcement learning, reward design, or training data curation for LLMs
• Comfortable managing technical vendor relationships and iterating quickly on feedback
• Strong project management and interpersonal skills
• Passionate about making AI more useful and accessible across different industries

Responsibilities

• Improve and execute 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