About Us
Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
The Role
In this role you will work in a small team of engineers and scientists to bring the benefits of scientific breakthroughs to the wider community with a focus on reducing the carbon footprint of energy generation.
Key Responsibilities
- Collaborate with engineers, product managers and scientific domain experts to develop and deploy machine learning models, data pipelines, and software in a production environment.
- Implement, test and iterate machine learning models.
- Obtaining and transforming data such that it can be useful for research.
- Keep up to date with the state of the art in power systems engineering and other relevant domains.
- Engage with Google DeepMind Research Scientists to identify potentially applicable techniques.
- Foundational work in AI for Climate projects.
- Contribute to external partnerships on behalf of Google DeepMind.
- Work in collaboration with our Responsibility 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 Engineer at Google DeepMind, we look for the following skills and experience:
- Experience with developing and deploying software in a production environment.
- Experience with at least one programming language (with a preference for those commonly used in machine learning or scientific computing such as Python and C++).
- Experience applying machine learning methods to real world data.
- Applied experience with machine learning, preferably modern deep learning architectures, in particular Graph Neural Networks.
- Knowledge of linear algebra, calculus and statistics equivalent to at least first-year university coursework.
- Experience exploring, analysing and visualising data.
- Experience working with large and noisy datasets.
In addition, the following would be an advantage:
- Scientific background in electricity systems & infrastructure.
- Experience collaborating across fields.
- Scientific knowledge (particularly physics).
- Experience using TensorFlow, Jax, NumPy, Pandas or similar ML/scientific libraries.
- Experience in open-source software and publicly-available code & the ability to ensure software artefacts (including codebases and data visualisations) meet open-source standards of readability, maintainability and reliability.