Tech Stack

Description

You will train base models through the complete post-training stack to deliver production Claude models, implementing techniques like Constitutional AI and RLHF. Your work directly impacts model quality, safety, and capabilities.

Requirements

• Strong software engineering skills with experience building complex ML systems
• Comfortable working with large-scale distributed systems and high-performance computing
• Experience with training, fine-tuning, or evaluating large language models
• Proficiency in Python, deep learning frameworks, and distributed computing
• Able to navigate ambiguity and make progress in fast-moving research environments

Responsibilities

• Implement and optimize post-training techniques at scale on frontier models
• Conduct research to develop and optimize post-training recipes that directly improve production model quality
• Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation
• Develop tools to measure and improve model performance across various dimensions
• Collaborate with research teams to translate emerging techniques into production-ready implementations