Research Scientist (Applied ML)
DeepMind’s mission is to solve intelligence, and use it to make the world a better place.
DeepMind Applied collaborates with experts to build tools that support their work in fields from healthcare to energy efficiency, and also applies DeepMind’s cutting-edge research to help improve Google’s products and services.
As an Applied research scientist working in ‘DeepMind for Google’, you’ll get the opportunity to collaborate closely with teams from across Google, working on projects of shared interest at Google scale. In addition, you will work on your own self-driven research agenda, motivated by the common challenges we see across the various problems the team is working on.
We use TensorFlow as our main infrastructure.
The role will suit candidates who enjoy metrics-driven work, and those who wish to immerse themselves in some of the most cutting-edge ML and AI research for real-world impact.
- Perform research to make machine learning (particularly reinforcement learning, deep learning and generative models) more applicable to real world problems.
- Provide research consultancy on applied projects: determine the scope of the problem, the best place to apply machine learning, and evaluate different approaches.
- Integrate new fundamental research ideas into applied projects.
- Collaborate with Software Engineers to design and run experiments, including designing and evaluating new algorithms as well as implementing known algorithms.
- Report and present experimental results and research findings clearly and efficiently, both internally and externally.
- PhD in computer science, mathematics, physics, electrical engineering, machine learning or equivalent
- Proven knowledge of machine learning and/or statistics.
- Flexible in choice of programming language. Competent in one or more of the Google supported languages (C++, Java, Python, Go) with a desire to learn more.
- Experience with implementing numerical methods and data visualization.
- Good knowledge of algorithm design.
- Strong expertise in reinforcement learning and / or deep learning.
- Experience with GPU programming.
- Experience with multi-threaded design and parallel/distributed computing.
- A passion for AI.