[Expression of Interest] Research Scientist / Engineer, Alignment Finetuning

About the role:

As a Research Scientist/Engineer on the Alignment Finetuning team at Anthropic, you'll lead the development and implementation of techniques aimed at training language models that are more aligned with human values: that demonstrate better moral reasoning, improved honesty, and good character. You'll work to develop novel finetuning techniques and to use these to demonstrably improve model behavior.

Note: For this role, we conduct all interviews in Python. We have filled our headcount for 2025. However, we are leaving this form open as an expression of interest since we expect to be growing the team in the future, and we will review your application when we do. As such, you may not hear back on your application to this team until the new year

Responsibilities:

• Develop and implement novel finetuning techniques using synthetic data generation and advanced training pipelines
• Use these to train models to have better alignment properties including honesty, character, and harmlessness
• Create and maintain evaluation frameworks to measure alignment properties in models
• Collaborate across teams to integrate alignment improvements into production models
• Develop processes to help automate and scale the work of the team

You may be a good fit if you:

• Have an MS/PhD in Computer Science, ML, or related field, or equivalent experience
• Possess strong programming skills, especially in Python
• Have experience with ML model training and experimentation
• Have a track record of implementing ML research
• Demonstrate strong analytical skills for interpreting experimental results
• Have experience with ML metrics and evaluation frameworks
• Excel at turning research ideas into working code
• Can identify and resolve practical implementation challenges

Strong candidates may also have:

• Experience with language model finetuning
• Background in AI alignment research
• Published work in ML or alignment
• Experience with synthetic data generation
• Familiarity with techniques like RLHF, constitutional AI, and reward modeling
• Track record of designing and implementing novel training approaches
• Experience with model behavior evaluation and improvement