OpenAI
Research
Researcher,Training
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Researcher, Training at OpenAI. Skills: LLM development, Model architecture, Model efficiency. Design new architectures. Prototype new architectures”
What You'll Achieve.
Enhancing intelligence; Enhancing efficiency; Adding new capabilities
Industry & Context.
Debugging thorny regressions; Tracking down bottleneck
Relocation assistance
What They're Looking For.
Must Have
Deep understanding of LLM architectures, Sophisticated understanding of model inference, Hands-on empirical approach
Nice to Have
Experience landing contributions to major LLM training runs, Can thoroughly evaluate and improve deep learning architectures, Well-versed in state of the art transformer modifications
What You'll Do.
Design new architectures
Prototype new architectures
Scale up new architectures
Study model performance
Debug model performance
Optimize model performance
Study computational performance
Debug computational performance
Optimize computational performance
Contribute to training infrastructure
Contribute to inference infrastructure
How You'll Work.
Team & Collaboration
Collaboratively analyze experiments
Full Job Description
ABOUT THE TEAM OpenAI's Training team is responsible for producing the large language models that power our research, our products, and ultimately bring us closer to AGI. Achieving this goal requires combining deep research into improving our current architecture and optimization techniques, alongside long-term bets aimed at improving the efficiency and capability of future generations of models. We are responsible for integrating these techniques and producing model artifacts used by the rest of the company, and ensuring that these models are world-class in every respect. ABOUT THE ROLE As a member of the training team, you will push the frontier of LLM development for OpenAI's flagship models, enhancing intelligence, efficiency, and adding new capabilities. Relevant interests may include areas such as architecture design, long-context and efficient attention, optimization and the science of scaling. Ideal candidates have a deep understanding of LLM architectures, a sophisticated understanding of model inference, and a hands-on empirical approach. A good fit for this role will be equally happy coming up with a creative breakthrough, investing in strengthening a baseline, designing an eval, debugging a thorny regression, or tracking down a bottleneck. This role is based in London. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. IN THIS ROLE, YOU WILL: - Design, prototype and scale up new architectures to improve model intelligence - Execute and analyze experiments autonomously and collaboratively - Study, debug, and optimize both model performance and computational performance - Contribute to training and inference infrastructure YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have experience landing contributions to major LLM training runs - Can thoroughly evaluate and improve deep learning architectures in a self-directed fashion - Are motivated by safely deploying LLMs in the real world - Are well-versed in th
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