Research Scientist, Machine Learning and Neuroscience

arch projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

To advance the field of artificial intelligence by exploring alternative computational paradigms beyond those currently trending. In particular, our team is interested in the discovery of learning algorithms for experimental, energy efficient hardware paradigms. We use both hand-design and automated discovery methods.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we're working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.

Responsibilities

• Explore thoroughly into a project for an extended period of time.

• Design, execute, and interpret machine learning experiments, selecting appropriate algorithms, models, and evaluation metrics.

• Review literature, identify key questions, think creatively, iterate on experiments, and employ scientific accuracy.

• Be proficient in one or more modern programming languages (e.g., Python), learn new programming languages. Learn technologies such as large-scale computation methods, be experienced with one or moremachine learning libraries (e.g., JAX or PyTorch).

• Write clear academic papers, give formal research talks, and have informal discussions with colleagues.