Research Engineer, Materials Science

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

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.

To succeed in this role you will need to be passionate about advancing science using machine learning and other computational techniques. You'll join an interdisciplinary team of domain experts, ML researchers and engineers exploring a diverse set of important scientific problems in materials science, physics, quantum chemistry 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. DM21 and GNoME). You'll leverage our unique mix of expertise, data and computational resources to experiment and iterate both rapidly and at scale.

As an embedded Research Engineer you will collaborate with researchers and software engineers to develop and run experiments exploring new applications of AI and LLMs to materials science. 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:

• Plan and perform rapid prototyping of machine learning techniques applied to problems in science.
• Undertake exploratory analysis to inform experimentation and research directions.
• Make improvements to model architectures and training procedures of machine learning models.
• Implement tools, libraries and frameworks to speed up and enable new research.
• Report and present software developments, experimental results and data analysis clearly and efficiently.
• Collaborate with internal and external scientific domain experts.
• 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, ML and AI research.

About You

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

• Degree in computer science, electrical engineering, science, mathematics or equivalent experience.
• Applied experience with modern deep learning techniques (e.g. Graph Neural Networks, Transformers, Diffusion).
• Experience working with large language models (LLMs).
• 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 large and noisy datasets.
• Experience using Jax, PyTorch, TensorFlow, NumPy, Pandas or similar ML/scientific libraries.

In addition, the following would be an advantage: 

• Experience with LLM post-training (RL or SFT).
• Experience developing custom LLM agents or tool-using systems.
• Specific domain expertise in areas like inorganic chemistry, solid-state physics, or materials synthesis.
• Experience running large-scale scientific simulations on Cloud or HPC clusters.
• Contributions to large infrastructure projec