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Form of workAt Google, we have a vision of empowerment and equitable opportunity for all Aboriginal and Torres Strait Islander peoples and commit to building reconciliation through Google’s technology, platforms and people and we welcome Indigenous applicants. Please see our Reconciliation Action Plan for more information.
Minimum qualifications:
- PhD in Computer Science, a related technical field, or equivalent practical experience
- 2 years of experience in Machine Learning (ML), ML Efficiency, ML Optimization, or a related field.
- Experience contributing to research communities including publishing in forums (e.g., ICML, ICLR, NeurIPS, or related)
- Experience with programming languages (e.g., Python or C/C++)
Preferred qualifications:
- Experience in theoretical and empirical research and abstracting and solving impactful research problems
- Ability to take initiative and drive new research ideas from problem abstraction, designing solution, experimentation, to productionization in a rapidly shifting landscape
- Excellent technical leadership and communication skills to conduct multi-team, cross-function collaborations
- Passion for deep/Machine Learning, computational statistics, and applied mathematics
About the job
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.
As a Research Scientist, you will participate in research in the domain of deep learning and large models. Potential areas of interest include development of new systems/architectures to improve capability of large foundational models, novel optimization algorithms to improve training and generalization of large models, advances to make inference with foundational models more efficient and flexible including knowledge adoption and distillation techniques, and novel Reinforcement Learning (RL) and similar techniques to make the models more safe, robust, and reliable.
In this role, you will have opportunities to collaborate with teams, enabling fundamental breakthroughs, and influencing next-generation AI-infused products reaching users.
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 engineers 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
- Abstract out key problems in the above mentioned areas, design elegant and deep solutions for these problems through theoretical and/or empirical insights, and build out prototypes/demos to showcase effectiveness of the designed solution.
- Lead and collaborate with research teams located across the globe.
- Drive and grow collaborations with product teams to land product innovations.
- Amplify impact and influence the research ecosystem through publications and scientific dissemination.
