Research Scientist, Large Scale Pre-Training Data

Lifelancer; London; Hybrid; GBP 80,000 - 100,000; Full time

Job summary

An innovative company is on the lookout for a passionate Research Scientist to join their dynamic team. This role offers the opportunity to engage in groundbreaking research focused on large scale pre-training data, contributing to the advancement of artificial intelligence. With a strong emphasis on empirical research and collaboration, you'll be at the forefront of developing new model capabilities and improving existing systems. If you have a solid background in deep learning and a desire to make a significant impact in the field, this position could be your next exciting challenge.

Qualifications

• PhD in machine learning or related field required.
• Hands-on experience with Python and large scale deep learning research.

Responsibilities

• Conduct empirical research to validate novel ideas for Gemini models.
• Collaborate with teams to improve understanding of training dynamics.

Skills

Deep Learning

Python

Neural Network Training

Data Scaling Laws

Distributed Systems

Communication Skills

Education

PhD in Machine Learning

Relevant Experience

Tools

Large Scale Data Processing Pipelines

Job description

Job Title: Research Scientist, Large Scale Pre-Training Data

Job Location: London, UK

Job Location Type: Hybrid

Job Contract Type: Full-time

Job Seniority Level: Associate

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Snapshot
At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. We are seeking a highly motivated Research Scientist to join our team and contribute to groundbreaking fundamental research and deployment in large scale pre-training.

About Us
Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The Role
We’re looking for a Research Scientist with a strong empirical and theoretical understanding of deep learning with a focus on data, as well as strong engineering skills and understanding of distributed systems.

Key responsibilities:

• Conduct careful empirical research to validate novel research ideas to improve performance of Gemini models.
• Develop strong intuitions grounded in data scaling laws and theoretical insights that can lead to research breakthroughs and new model capabilities.
• Dive deep into specific areas of the pre-training stack to improve our understanding of large scale training dynamics.
• Collaborate with the wider Gemini team, engaging closely with the Model, Infrastructure and the Post-Training teams.

About You
In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:

• A PhD in machine learning or closely related field, or similar experience.
• A proven track record of large scale deep learning research with hands-on experience with Python and neural network training (publications, open-source projects, relevant work experience, …).
• An in-depth knowledge of large scale training dynamics.
• Ability to communicate technical ideas effectively, e.g. through discussions, whiteboard sessions, written documentation.

In addition, the following would be an advantage:

• Experience with large scale data processing pipelines.
• Experience with distributed systems and large scale deep learning performance optimisation.
• Experience with running large scale data processing pipelines.