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

You may be a good fit if you:

• Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities

• Adapt quickly to changing priorities

• Maintain clarity when debugging complex, time-sensitive issues

• 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

Strong candidates may also:

• Have experience with LLMs

• Have a keen interest in AI safety and responsible deployment

We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems. However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.