About the Role
At Google DeepMind, science is at the heart of everything they do. From the beginning, they took inspiration from science to build better algorithms, and now, they want to use their toolkit to accelerate scientific discovery. By bringing together specialists with backgrounds in machine learning, computer science, physics, chemistry, biology and more, they’re optimistic that they 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. Their work is organised into several longer-term focus areas, which aim to achieve step changes to the state-of-the-art.
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.