Researcher, Training - London

OpenAI; Greater London; Ibrido; GBP 80.000 - 110.000; Tempo pieno

Vantaggi offerti da questo lavoro

Relocation assistance

Hybrid work model

Descrizione del lavoro

OpenAI is seeking a candidate for LLM development, responsible for enhancing intelligence and efficiency of language models. The role involves designing new architectures and contributing to model performance optimization.

Based in London, the position follows a hybrid work model with relocation assistance. The ideal candidate should have a deep understanding of LLM architectures and skills in debugging and optimizing model performance.

Competenze

• Deep understanding of LLM architectures.
• Hands-on experience in scaling model intelligence.
• Ability to analyze experiments and debug model performance.

Mansioni

• Design, prototype and scale new architectures for model intelligence.
• Execute and analyze experiments autonomously.
• Contribute to training and inference infrastructure.

Conoscenze

Understanding of LLM architectures

Deep learning architecture evaluation

Model inference understanding

Debugging and optimization skills

Descrizione del lavoro

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 the state‑of‑the‑art transformer modifications for efficiency.

Workplace & Location:

This role is based in our London office, and we aren’t considering applications to work remotely at this time.

If you're joining us in person, we offer relocation support and follow a hybrid schedule: three days a week in the office, with the option to work from home on Thursdays and Fridays. Our offices are set up for focus and connection—with adjustable desks, phone booths, conference rooms, stocked kitchens, and cozy spots to unwind or catch up with teammates.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.