Research Scientist, Machine Learning, Structural & Synthetic Biology

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. We constantly iterate on our workplace experience with the goal of ensuring it encourages a balanced life. From excellent office facilities through to extensive manager support, we strive to support our people and their needs as effectively as possible.

The Role

As a Research Scientist in the Science Program, you will use your machine learning expertise to collaborate with domain experts and other machine learning scientists within our natural science programs. The domain of focus in this role is structural biology and synthetic biology.

Key Responsibilities

• Engage with existing research efforts in the Science Program to solve key challenges using your broad machine learning experience.
• Collaborate with researchers and machine learning engineers to identify and develop novel machine learning 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 programme.
• Work in collaboration with our Ethics and Governance teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

About You

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

• PhD demonstrating significant advances in machine learning or equivalent practical experience.
• Passion for accelerating science using innovative technologies.
• Programming experience.
• Quantitative skills in maths and statistics.
• Experience of common scripting languages or pipelining tools.

In addition, the following would be an advantage:

• Experience in applying machine learning techniques to problems in the natural sciences, e.g. on proteins or molecules.
• Experience in generative machine learning techniques, such as language modelling.
• Experience in geometric deep learning.
• Experience in machine learning on real world, experimental data.
• Shown success in delivering high quality research impact.
• A real passion for AI!