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 disciplines
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Can navigate ambiguity and make progress in fast-moving research environments
Strong candidates may also:
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.
