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Research Engineer, Production Model Post-Training

Anthropic· AI Research & Engineering
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Zürich, CHPosted 8mo ago

About the role

Research Engineer focused on training base models through the full post-training stack to deliver production Claude models. Works at the intersection of research and production engineering to implement, scale, and improve post-training techniques (e.g., Constitutional AI, RLHF) for high-quality, safe production models.

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

Qualifications

  • Strong software engineering skills with experience building complex ML systems
  • Experience with training, fine-tuning, or evaluating large language models
  • Comfortable working with large-scale distributed systems and high-performance computing
  • Proficiency in Python, deep learning frameworks, and distributed computing
  • Ability to balance research exploration with engineering rigor and operational reliability
  • A track record of analyzing and debugging model training processes
  • Willingness to collaborate across research and engineering disciplines
  • Ability to navigate ambiguity and make progress in fast-moving research environments

Skills

Required:PythonDeep learning frameworksDistributed computingModel fine-tuning and evaluation
Last updated Jul 8, 2026. Interested? Apply on the company site.

About Anthropic

building reliable, interpretable, and steerable AI systems

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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