Staff Research Scientist, Machine Learning Efficiency Location: Sydney NSW, Australia Advanced Qualifications Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.
Minimum Requirements Ph D in Computer Science, a related technical field, or equivalent practical experience.
4 years of experience with research agendas across multiple teams or projects in Machine Learning (ML), ML Efficiency, ML Optimization, or a related field.
Experience with programming languages (e.g., Python or C/C++).
One or more scientific publication submissions for conferences, journals, or public repositories (e.g., ICML, ICLR, Neur IPS).
Preferred Qualifications Experience in innovative research.
Experience as a leader within a research team.
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 set up 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.
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 collaborations with research programs at partner universities and technical institutes worldwide.
Our team is committed to advancing in the areas of efficient architectures, training efficiency of foundational models, data efficiency, and inference efficiency.
In this role, you will have opportunities to collaborate with Google teams across the world and advance in the above-mentioned areas, enable fundamental breakthroughs, and pioneer next-generation products.
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 fundamental advances in algorithms and foundational model architectures that improve the speed of training and generalization of deep learning models.
Develop fundamental advances to make inference with foundational models more efficient and flexible including knowledge adoption and distillation techniques.
Work on data subset selection and more efficient ways to train with large data sets.
Improve the entire model deployment pipeline including formulations for pre-training, instruction and tuning, and Reinforcement Learning from Human Feedback (RLHF).
Google is proud to be an equal opportunity and affirmative action employer.
We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing equal employment opportunities regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law.
Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
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