Research Engineer, Materials Science

At Google DeepMind, we're committed to equal employment opportunities and value diversity of experience, knowledge, backgrounds, and perspectives. We're 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.

As a Research Engineer, Materials Science, 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.

Key responsibilities:

• Use your domain knowledge in the sciences (if applicable) to design, develop, and implement high-performance simulations, tools, and analysis workflows.
• Apply your software engineering expertise to produce high-quality, reusable code and components to tackle meaningful strategic problems.
• Share ideas with other specialists in the team and be highly collaborative, striving to cultivate a culture of continuous development and advancement.
• Employ cutting-edge technology and techniques to contribute to solving some of the hardest problems.
• Incorporate your passion for software engineering and high-performance computing to enable running scientific calculations at scale and accelerate scientific discovery.

About you:

• Dedicated software engineer with experience in software design and development, obtained either through a degree or applied experience.
• Proven experience in Python, C++, and interoperability between the two.
• Experience with concurrent and distributed software algorithms and architectures.
• Experience applying software engineering principles in a scientific research environment.

In addition, one or more of the following would be strongly preferred:

• Scientific knowledge (particularly materials science, chemistry, or physics).
• Experience with high-performance computing (HPC) and running high-throughput scientific simulations at scale.
• Applied experience with scientific simulations (e.g., molecular dynamics, computational chemistry simulations, etc.).
• Applied experience with modern deep learning architectures (e.g., transformers, diffusion models).