Research Engineer, Production Model Post-Training

About the Role

Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with.

You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models.

*Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends.*

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
• Debug complex issues in training pipelines and model behavior
• Help establish best practices for reliable, reproducible model post-training

Qualifications

• 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
• Ability to balance research exploration with engineering rigor and operational reliability
• Adept at analyzing and debugging model training processes
• Ability to thrive in controlled chaos, navigate ambiguity, and adapt quickly to changing priorities
• Enjoy collaborating across research and engineering disciplines

Preferred Qualifications

• Hands-on experience with frontier AI systems
• Keen interest in AI safety and responsible deployment