Researcher, Training - London

OpenAI OpenAI · AI Frontier · London, United Kingdom · Research

Researcher focused on pushing the frontier of LLM development, enhancing intelligence, efficiency, and adding new capabilities through research into architecture design, long-context, efficient attention, optimization, and scaling. The role involves designing, prototyping, and scaling new architectures, executing and analyzing experiments, optimizing model and computational performance, and contributing to training and inference infrastructure. Experience with major LLM training runs and evaluating deep learning architectures is desired.

What you'd actually do

  1. Design, prototype and scale up new architectures to improve model intelligence
  2. Execute and analyze experiments autonomously and collaboratively
  3. Study, debug, and optimize both model performance and computational performance
  4. Contribute to training and inference infrastructure

Skills

Required

  • deep understanding of LLM architectures
  • sophisticated understanding of model inference
  • hands-on empirical approach
  • experience landing contributions to major LLM training runs
  • thoroughly evaluate and improve deep learning architectures in a self-directed fashion
  • motivated by safely deploying LLMs in the real world
  • well-versed in the state of the art transformer modifications for efficiency

Nice to have

  • architecture design
  • long-context and efficient attention
  • optimization and the science of scaling

What the JD emphasized

  • push the frontier of LLM development
  • deep research into improving our current architecture and optimization techniques
  • long-term bets aimed at improving the efficiency and capability of future generations of models
  • integrating these techniques and producing model artifacts
  • design, prototype and scale up new architectures
  • execute and analyze experiments autonomously
  • study, debug, and optimize both model performance and computational performance
  • contribute to training and inference infrastructure
  • landing contributions to major LLM training runs
  • thoroughly evaluate and improve deep learning architectures in a self-directed fashion
  • safely deploying LLMs in the real world
  • well-versed in the state of the art transformer modifications for efficiency

Other signals

  • push the frontier of LLM development
  • integrating these techniques and producing model artifacts
  • design, prototype and scale up new architectures