About the job
About Anthropic
At Anthropic, we are dedicated to developing AI systems that are safe, interpretable, and controllable. Our goal is to ensure that AI technology is beneficial for our users and society at large. Our rapidly expanding team comprises passionate researchers, engineers, policy experts, and business leaders collaborating to create AI solutions that have a positive impact.
About the Role
As a Research Engineer in our Post-Training team, you will play a pivotal role in enhancing the capabilities, alignment, and safety of our production models through advanced post-training methodologies. You will be responsible for training our foundational models using a comprehensive post-training pipeline, ultimately delivering the Claude models that our users interact with.
Your work will be at the nexus of cutting-edge research and production engineering, where you will implement, scale, and refine post-training techniques such as Constitutional AI, RLHF, and various alignment methodologies. Your contributions will directly influence the quality, safety, and performance of our production models.
Note: All interviews for this position will be conducted using Python. This role may require on-call responses to incidents, including weekends.
Responsibilities:
Implement and optimize post-training techniques at scale for advanced models.
Conduct research to develop and refine post-training strategies that enhance production model quality.
Design, build, and manage robust, efficient pipelines for model fine-tuning and evaluation.
Create tools to assess and enhance model performance across various metrics.
Collaborate with research teams to translate innovative techniques into production-ready solutions.
Troubleshoot complex issues within training pipelines and model behavior.
Establish best practices for reliable and reproducible model post-training processes.
You May Be a Good Fit If You:
Thrive in dynamic environments and feel energized by managing multiple urgent tasks.
Quickly adapt to shifting priorities.
Maintain composure while debugging intricate, time-sensitive issues.
Possess strong software engineering skills with experience in developing complex ML systems.
