Staff Research Scientist, Machine Learning

1 week ago

Sydney, Australia Google Full time

**Minimum qualifications**:

- PhD 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, NeurIPS).

**Preferred qualifications**:

- Experience in innovative research.
- Experience as a leader within a research team.
- Ability to effectively navigate ambiguity.

About the job
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 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 over 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 workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See alsoGoogle's EEO Policy andEEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing ourAccommodations for Applicants form.

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