Research Engineer, Production Model Post-Training at Anthropic

Hybrid - San Francisco, CA; New York City, NY; Seattle, WA

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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 will train 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 such as Constitutional AI and RLHF. Your work will directly impact the quality, safety, and capabilities of our production models.

Salary

USD 350,000 - 500,000

Requirements

Skills

  • Thrive in controlled chaos and are energized, 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
  • Have experience with LLMs
  • Have a keen interest in AI safety and responsible deployment
  • Proficiency in Python, deep learning frameworks, and distributed computing
  • Bachelor’s degree or an equivalent combination of education, training, and/or experience
  • Relevant field of study

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

Technologies

PythonConstitutional AIRLHFLarge language modelsDistributed computingDeep learning frameworks

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