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Senior Research Scientist, Machine Learning Efficiency
3 months ago
Info:
At Google, we have a vision of empowerment and equitable opportunity for all Aboriginal and Torres Strait Islander peoples. We are committed 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.
- 7 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 innovative research.
- Experience working with a research team.
- Ability to effectively navigate ambiguity.
- Excellent coding skills.
About the role:
Google envisions empowerment and equal opportunities for Aboriginal and Torres Strait Islander people. As a Research Scientist, you will drive research projects focusing on fundamental research, new product innovation, and infrastructure goals. You will have the freedom to emphasize specific types of work, manage deadlines, and deploy promising ideas quickly and broadly. Applying the latest theories, you will develop new and improved products, processes, or technologies.
You will work on real-world problems covering various computer science areas like machine learning, data mining, natural language processing, performance analysis, and more. Collaboration with universities and technical institutes worldwide will be part of your responsibilities to contribute to the wider research community.
Your role involves advancing in efficient architectures, training efficiency, data efficiency, and inference efficiency. Collaborating with Google teams globally, you will pioneer next-generation products and fundamental breakthroughs in various areas.
Google Research aims to build intelligent systems for all Google products, utilizing computer science techniques developed by skilled software developers and research scientists. The teams collaborate closely, adapting to new projects and business demands.
Responsibilities:
- Develop fundamental advances in algorithms and foundational model architectures to enhance the speed of training and generalization of deep learning models.
- Advance techniques for efficient inference with foundational models, including knowledge adoption and distillation.
- Enhance data subset selection and improve ways to train with large datasets efficiently.
- Optimize the model deployment pipeline, including better pretraining formulations, instruction and tuning, and Reinforcement Learning from Human Feedback (RLHF).
