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 develop and optimize the systems that transform our base models into the refined 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.
Responsibilities:
- Implement and optimize post-training techniques at scale on frontier models
- 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
You may be a good fit if you:
- Have strong software engineering skills with experience building complex ML systems
- Are comfortable working with large-scale distributed systems and high-performance computing
- Have experience with training, fine-tuning, or evaluating large language models
- Can balance research exploration with engineering rigor and operational reliability
- Are adept at analyzing and debugging model training processes
- Enjoy collaborating across research and engineering disciplines
- Can navigate ambiguity and make progress in fast-moving research environments
- Have a keen interest in AI safety and responsible deployment
- Experience with LLMs is a significant plus
- Proficiency in Python, deep learning frameworks, and distributed computing is required for this role
We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems.
