Staff Research Scientist, ML Efficiency, Google Research
Staff Research Scientist, ML Efficiency, Google Research
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Minimum qualifications:
- PhD degree in Computer Science, a related field, or equivalent practical experience.
- 4 years of experience in a university or industry labs, with Artificial Intelligence (AI) research.
- One of more scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Preferred qualifications:
- Experience with deep/machine learning, computational statistics, and applied mathematics.
- Knowledge of transformer architecture internals.
- Ability to drive new research ideas from problem abstraction, designing solutions, experimentation, to productionisation in a rapidly shifting landscape.
- Excellent technical leadership and communication skills to conduct multi-team cross-function collaborations.
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.
Google Research Singapore is the very latest addition to the Google Research presence around the globe!
In this role, you will be making significant breakthroughs towards Computational Efficiency of large-scale Generative AI Models (LLMs, Diffusion Models, Generative Videos).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
- Advance algorithms, sampling techniques and large-scale optimization to make serving and inference of generative AI models more efficient and flexible.This includes model compression, knowledge distillation and quantization strategies.
- Innovate algorithms and large language model architectures that improve computation efficiency and generalization of training deep learning models.
- Improve the end-to-end model deployment pipeline that includes entirely new formulations of pretraining, instruction tuning, reinforcement learning, thinking and reasoning.
- Collaborate with hardware and software teams to optimize kernels and inference engines, across different hardware and model architectures.
- Optimize latency, memory bandwidth, workloads.
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