Snapshot

Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning and systems neuroscience to build general-purpose learning algorithms. We have already made a number of high profile breakthroughs towards building artificial general intelligence, and we have all the ingredients in place to make further significant progress over the coming years.

About Us

We’re a dedicated scientific community, committed to “solving intelligence” and ensuring our technology is used for widespread public benefit.

We’ve built a supportive and inclusive environment where collaboration is encouraged and learning is shared freely. We don’t set limits based on what others think is possible or impossible. We drive ourselves and inspire each other to push boundaries and achieve ambitious goals.

The Role

To succeed in this role you will need to be passionate about advancing science using recent breakthroughs in large language models, in addition to standard machine learning and other computational techniques. You will join an interdisciplinary team of domain experts, ML researchers and engineers exploring a diverse set of important scientific problems in biology, physics, mathematics and other areas. Our work is organised into several longer-term focus areas which aim to achieve step changes to the state-of-the-art (as exemplified in e.g. AlphaFold, AlphaMissense and FunSearch). You'll leverage our unique mix of expertise, data and computational resources to experiment and iterate both rapidly and at scale.

As a Research Scientist in Science, you will use your machine learning expertise to collaborate with domain experts and machine learning scientists to develop and run experiments exploring new applications of AI - particularly LLMs - to science problems. The team is pioneering in many different domains so you may take part in exploratory work validating early ideas or work in a maturing area to deepen and exploit a promising line of research. You may also contribute to the scientific knowledge and experience of the team with your own scientific domain knowledge. You will work with internal and external researchers on pioneering research bridging AI and science.

Key responsibilities:

• Research the development and application of natural language LLMs for scientific problems.
• Plan and perform rapid prototyping of large language model techniques applied to problems in science.
• Engage with both underlying data sources (including diverse scientific text, figures, and multimodal data) and models.
• Undertake exploratory analysis to inform experimentation and research directions.
• Collaborate with researchers and machine learning engineers to identify and develop novel large language model approaches tailored to natural sciences.
• Report and present research findings and developments (including status and results) clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet research goals for the wider science program.
  
  

The role will suit candidates who enjoy working in a heavily experimental setting with large and noisy datasets and who wish to immerse themselves in innovative science, LLM, ML and AI research.

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:

• PhD degree or equivalent experience in computer science, electrical engineering, natural science, or mathematics.
• Experience working with large language models (LLMs), e.g. fine-tuning and inference.
• Experience working with large and noisy datasets.
• Experience with at least one programming language (with a preference for those commonly used in machine learning or scientific computing such as Python or C++).
• Knowledge of linear algebra, calculus and statistics equivalent to at least first-year university coursework.
• Experience exploring, analysing and visualising data.
• Experience using Jax, PyTorch, NumPy, Pandas or similar ML/scientific libraries.
  
  

In addition, the following would be an advantage:

• Experience with advanced recent LLM techniques.
• Scientific domain knowledge (particularly biology, physics, or chemistry).
  
  

When assessing technical background we will take a holistic view of the mix of scientific, ML and computational experience. We do not expect you to be an expert in all fields simultaneously. However, except for scientific knowledge, since the role serves as a bridge between all three, some experience in each is necessary.