About the role
From the employer’s listing · Anthropic · posted 28 August 2026
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