Research Scientist – ML Efficiency
Google · Singapour
Job description
About the role
Google Research Singapore is seeking a Research Scientist to drive breakthroughs in computational efficiency for large‑scale generative AI models such as large language models, diffusion models, and generative video systems. You will translate cutting‑edge research ideas into scalable experiments, prototypes, and production‑ready solutions.
Key responsibilities
- Design and execute large‑scale tests, rapidly deploying promising concepts across Google products.
- Develop new model architectures and algorithms that improve efficiency, compression, and inference speed.
- Collaborate with cross‑functional teams to integrate research outcomes into real‑world systems.
- Publish findings in top conferences and journals, and contribute to the broader research community.
- Focus on algorithmic efficiency, model compression, and inference acceleration for generative AI.
Required profile
- PhD in Computer Science, a related field, or equivalent practical experience.
- At least one peer‑reviewed publication in venues such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.
- Experience in university or industry labs with a primary emphasis on AI research.
- Deep understanding of transformer architecture internals.
- Proven ability to take research ideas from abstraction through design, experimentation, and productionisation.
- Excellent technical leadership and communication skills for multi‑team collaborations.
- Strong passion for deep learning, computational statistics, and applied mathematics.
Required skills
- Transformer architecture
- Machine learning
- Deep learning
- Computational statistics
- Applied mathematics
- Algorithmic efficiency
- Model compression
- Inference acceleration
What we offer
- Opportunity to work on high‑impact AI research that shapes products used by billions.
- Access to Google’s extensive computational resources and interdisciplinary teams.
- Support for publishing and presenting work at leading conferences.
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Published 1 week ago
Expires 1 month from now
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