Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
- 2 years of experience in implementing Machine Learning algorithms or techniques (e.g., basics of automatic differentiation) and in using Machine Learning frameworks (e.g., Jax, PyTorch, Triton).
- One or more scientific publication submission(s) in Artificial Life conferences (e.g., ALIFE, GECCO, NeurIPS), journals and repositories or equivalent publications in similar fields such as Machine Learning, Mathematics or Physics.
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- PhD in Computer Science or a related technical field.
- Experience in using Google Collaboratory or similar interactive Integrated Development Environments (IDEs) to disseminate and share research work in progress.
- Experience in deep learning, evolutionary strategies, or reinforcement learning.
- Experience owning and initiating research agendas.
- Knowledge of linear algebra, statistics, and calculus relating to the underlying principles of Artificial Intelligence algorithms.
- Ability to conduct independent research, exploring unconventional ideas in the field of Artificial Life.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
In this role, you will be performing research on self-organizing systems, in a Research team which conducts both theoretical and empirical research in neural computation and fundamentally rethinking approaches to intelligence. Their activity involves improving, leveraging, and benchmarking emerging substrates for intelligence, using models such as Neural Cellular Automata, memory-local compute paradigms and novel/niche approaches exhibiting complexification and open-endedness to achieve this goal. They are exploring and building paradigms of intelligence that either scale exceptionally well on existing hardware or easily map to new hardware.
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
- Develop machine learning code, interactive demos or prototypes to assess the feasibility and performance of potential research ideas involving advances in creating in-silico intelligence with real-life utility.
- Contribute to research publications in the form of articles as well as publications in traditional venues.
- Implement theoretical brainstorming and experimentation in code or hardware.