Research Scientist, Paradigms of Intelligence

As an organization, Google maintains a portfolio of research 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. Our team conducts basic research into alternative AI paradigms beyond those currently trending. Our goal is to discover novel AI algorithms that can be efficient to run on typical or alternate computing substrates, using a mix of automated, hand-designed, and hybrid methods—specifically focusing on how advancing Large Language Model (LLM)-related techniques can accelerate this process. In this role, you will research, develop, and publish breakthroughs in both algorithm discovery methods and the resulting algorithms themselves. The Technology & Society organization connects research, people, and ideas across Google and Alphabet to help shape and advance our most ambitious technology innovations and initiatives and their impact on users and society for the better, and responsibly. In addition, we also aim to share perspectives, engage, and collaborate with others externally on technology related issues and opportunities for society. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. Canada: $150000 - $153000 (CAD) + 15% bonus target + equity + benefits US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . Carry out sustained exploratory research. Apply the results of your research. Implement and share research findings with researchers and engineers. Review literature, identify key questions, design experiments, and interpret results. Collaborate in person and remotely; maintain a respectful work environment. Share ideas verbally and in writing; publish and present work at journals or scientific conferences. Minimum qualifications: PhD degree in Computer Science, a related field, or equivalent practical experience. Recent and repeated evidence of independent research, shown through two or more first-authored scientific publications at the main track of the machine learning conferences (NeurIPS, ICML, or ICLR), or of the natural language processing conferences (ACL, NAACL, or EMNLP), published since 2021. Recent and repeated evidence of experience in large language model research (LLMs, agents, foundation models, LLM training or fine-tuning, transformers, or strongly related), shown through two or more scientific publications, published since 2021. The same papers can satisfy more than one minimum qualification. Preferred qualifications: Post-doctoral experience. Experience in the use of LLMs in fields of program synthesis, automated code discovery, AutoML, or automated science. Experience in training and fine-tuning LLMs. Experience in programming with C++. Excellent Python programming expertise, including machine learning Python libraries such as JAX, TensorFlow or PyTorch. PhD degree in Computer Science, a related field, or equivalent practical experience. Recent and repeated evidence of independent research, shown through two or more first-authored scientific publications at the main track of the machine learning conferences (NeurIPS, ICML, or ICLR), or of the natural language processing conferences (ACL, NAACL, or EMNLP), published since 2021. Recent and repeated evidence of experience in large language model research (LLMs, agents, foundation models, LLM training or fine-tuning, transformers, or strongly related), shown through two or more scientific publications, published since 2021. The same papers can satisfy more than one minimum qualification.