About the Role
- Science is at the heart of everything we do at Google DeepMind. From the beginning, we took inspiration from science to build better algorithms, and now, we want to use our toolkit to accelerate scientific discovery. By bringing together specialists with backgrounds in machine learning, computer science, physics, chemistry, biology and more, we’re optimistic that we can build new methods that will push the boundaries of what is possible and help solve the biggest problems facing humanity.
- Google DeepMind (GDM) is pursuing a ground-breaking research program in materials, aiming to accelerate the discovery of new functional materials by combining the predictive power of artificial intelligence (AI) and computational simulation with automated experimentation. 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).
- To succeed in this role you will need to be passionate about advancing material science using machine learning and other computational techniques. As an embedded Research Engineer you will collaborate with other researchers and engineers to develop infrastructure for running experiments and help researchers explore new applications of AI and LLMs to materials science. The team is pioneering in many different domains so you will take part in exploratory work that enables validating early ideas, and work in a maturing area to deepen and build infrastructure to exploit a promising line of research. You will also contribute to the scientific knowledge and experience of the team with your own scientific domain knowledge.
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.
Requirements
- Degree in computer science, electrical engineering, science, mathematics or equivalent experience.
- Experience applying software engineering principles in a scientific research environment.
- 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.
Qualifications
- Specific domain expertise in areas like inorganic chemistry, solid-state physics, or materials synthesis.
- Experience applying modern deep learning architectures (e.g., transformers, diffusion models) to chemistry or material science challenges (e.g. ML force fields).
- Experience running large-scale scientific simulations (e.g. molecular dynamics, computational chemistry simulations, etc.) on Cloud or HPC clusters.
- Experience developing custom LLM agents or tool-using systems.
- Experience with concurrent and distributed software algorithms and architectures.
- Masters or PhD in computer science, electrical engineering, science, mathematics or equivalent experience.
Benefits
The US base salary range for this full-time position is between $141,000 - $202,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.