Research Engineer, Production Model Post-Training

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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:

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Implement and optimize post-training techniques at scale on frontier models

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Conduct research to develop and optimize post-training recipes that directly improve production model quality

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Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation

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Develop tools to measure and improve model performance across various dimensions

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Collaborate with research teams to translate emerging techniques into production-ready implementations

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Debug complex issues in training pipelines and model behavior

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Help establish best practices for reliable, reproducible model post-training

You may be a good fit if you:

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

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Adapt quickly to changing priorities

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Maintain clarity when debugging complex, time-sensitive issues

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Have strong software engineering skills with experience building complex ML systems

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Are comfortable working with large-scale distributed systems and high-performance computing

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Have experience with training, fine-tuning, or evaluating large language models

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Can balance research exploration with engineering rigor and operational reliability

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Are adept at analyzing and debugging model training processes

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Enjoy collaborating across research and engineering di